{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "   ###  2D Stochastic Gaussian Simulation in Python for Engineers and Geoscientists \n",
    "\n",
    "### with GSLIB's SGSIM Program Converted to Python\n",
    "\n",
    "#### Michael Pyrcz, Associate Professor, University of Texas at Austin \n",
    "\n",
    "\n",
    "#### Contacts: [Twitter/@GeostatsGuy](https://twitter.com/geostatsguy) | [GitHub/GeostatsGuy](https://github.com/GeostatsGuy) | [www.michaelpyrcz.com](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446)\n",
    "\n",
    "This is a tutorial for / demonstration of **Kriging in Python with GSLIB's KB2D program translated to Python, wrappers and reimplementations of other GSLIB: Geostatistical Library methods** (Deutsch and Journel, 1997). \n",
    "\n",
    "This exercise demonstrates the kriging method in Python with wrappers and reimplimentation of GSLIB methods. The steps include:\n",
    "\n",
    "1. generate a 2D model with sequential Gaussian simulation\n",
    "2. sample from the simulation\n",
    "3. calculate and visualize the kriged map\n",
    "\n",
    "To accomplish this I have provide wrappers or reimplementation in Python for the following GSLIB methods:\n",
    "\n",
    "1. sgsim - sequantial Gaussian simulation limited to 2D and unconditional\n",
    "2. hist - histograms plots reimplemented with GSLIB parameters using python methods\n",
    "3. locmap - location maps reimplemented with GSLIB parameters using python methods\n",
    "4. pixelplt - pixel plots reimplemented with GSLIB parameters using python methods\n",
    "5. locpix - my modification of GSLIB to superimpose a location map on a pixel plot reimplemented with GSLIB parameters using Python methods\n",
    "5. affine - affine correction adjust the mean and standard deviation of a feature reimplemented with GSLIB parameters using Python methods\n",
    "\n",
    "I have also started to translate the GSLIB support subfunctions to Python. Stay tuned.\n",
    "\n",
    "The GSLIB source and executables are available at http://www.statios.com/Quick/gslib.html.  For the reference on using GSLIB check out the User Guide, GSLIB: Geostatistical Software Library and User's Guide by Clayton V. Deutsch and Andre G. Journel.  Overtime, more of the GSLIB programs will be translated to Python and there will be no need to have the executables.  For this workflow you will need sgsim.exe from GSLIB.com for windows and Mac OS executables from https://github.com/GeostatsGuy/GSLIB_MacOS.  \n",
    "\n",
    "I did this to allow people to use these GSLIB functions that are extremely robust in Python. Also this should be a bridge to allow so many familar with GSLIB to work in Python as a kept the parameterization and displays consistent with GSLIB.  The wrappers are simple functions declared below that write the parameter files, run the GSLIB executable in the working directory and load and visualize the output in Python. This will be included on GitHub for anyone to try it out https://github.com/GeostatsGuy/.  \n",
    "\n",
    "This was my first effort to translate the GSLIB Fortran to Python.  It was pretty easy so I'll start translating other critical GSLIB functions.  I've completed NSCORE, DECLUS, GAM, GAMV and now KB2D as of now.\n",
    "\n",
    "#### Load the required libraries\n",
    "\n",
    "The following code loads the required libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os                                                 # to set current working directory \n",
    "import numpy as np                                        # arrays and matrix math\n",
    "import pandas as pd                                       # DataFrames\n",
    "import matplotlib.pyplot as plt                           # plotting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you get a package import error, you may have to first install some of these packages. This can usually be accomplished by opening up a command window on Windows and then typing 'python -m pip install [package-name]'. More assistance is available with the respective package docs.  \n",
    "\n",
    "#### Declare functions\n",
    "\n",
    "Here are the wrappers and reimplementations of GSLIB method along with two utilities to load GSLIB's Geo-EAS from data files into DataFrames and 2D Numpy arrays.  These are used in the testing workflow.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Some GeostatsPy Functions - by Michael Pyrcz, maintained at https://git.io/fNgR7.\n",
    "# A set of functions to provide access to GSLIB in Python.\n",
    "# GSLIB executables: nscore.exe, declus.exe, gam.exe, gamv.exe, vmodel.exe, kb2d.exe & sgsim.exe must be in the working directory \n",
    "# note, since I have now rewritten nscore, gam, gamv and kb2d one can just use these in Python\n",
    "# available in the geostatspy package.\n",
    "import pandas as pd\n",
    "import os\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt                          \n",
    "import random as rand\n",
    "image_type = 'tif'; dpi = 600\n",
    "\n",
    "# utility to convert GSLIB Geo-EAS files to a 1D or 2D numpy ndarray for use with Python methods\n",
    "def GSLIB2ndarray(data_file,kcol,nx,ny): \n",
    "    colArray = []\n",
    "    if ny > 1:\n",
    "        array = np.ndarray(shape=(ny,nx),dtype=float,order='F')\n",
    "    else:\n",
    "        array = np.zeros(nx)    \n",
    "    with open(data_file) as myfile:   # read first two lines\n",
    "        head = [next(myfile) for x in range(2)]\n",
    "        line2 = head[1].split()\n",
    "        ncol = int(line2[0])          # get the number of columns\n",
    "        for icol in range(0, ncol):   # read over the column names\n",
    "            head = [next(myfile) for x in range(1)]\n",
    "            if icol == kcol:\n",
    "                col_name = head[0].split()[0]       \n",
    "        if ny > 1:\n",
    "            for iy in range(0,ny):\n",
    "                for ix in range(0,nx):\n",
    "                    head = [next(myfile) for x in range(1)]\n",
    "                    array[ny-1-iy][ix] = head[0].split()[kcol]\n",
    "        else:\n",
    "            for ix in range(0,nx):\n",
    "                head = [next(myfile) for x in range(1)]\n",
    "                array[ix] = head[0].split()[kcol]\n",
    "    return array,col_name \n",
    "\n",
    "# utility to convert GSLIB Geo-EAS files to a pandas DataFrame for use with Python methods\n",
    "def GSLIB2Dataframe(data_file):\n",
    "    colArray = []\n",
    "    with open(data_file) as myfile:   # read first two lines\n",
    "        head = [next(myfile) for x in range(2)]\n",
    "        line2 = head[1].split()\n",
    "        ncol = int(line2[0])\n",
    "        for icol in range(0, ncol):\n",
    "            head = [next(myfile) for x in range(1)]\n",
    "            colArray.append(head[0].split()[0])\n",
    "        data = np.loadtxt(myfile, skiprows = 0)\n",
    "        df = pd.DataFrame(data)\n",
    "        df.columns = colArray\n",
    "        return df\n",
    "\n",
    "# histogram, reimplemented in Python of GSLIB hist with MatPlotLib methods, displayed and as image file\n",
    "def hist(array,xmin,xmax,log,cumul,bins,weights,xlabel,title,fig_name):\n",
    "    plt.figure(figsize=(8,6))\n",
    "    cs = plt.hist(array, alpha = 0.2, color = 'red', edgecolor = 'black', bins=bins, range = [xmin,xmax], weights = weights, log = log, cumulative = cumul)\n",
    "    plt.title(title)\n",
    "    plt.xlabel(xlabel); plt.ylabel('Frequency')  \n",
    "    plt.savefig(fig_name + '.' + image_type,dpi=dpi)\n",
    "    plt.show()\n",
    "    return\n",
    "\n",
    "# histogram, reimplemented in Python of GSLIB hist with MatPlotLib methods (version for subplots)\n",
    "def hist_st(array,xmin,xmax,log,cumul,bins,weights,xlabel,title):  \n",
    "    cs = plt.hist(array, alpha = 0.2, color = 'red', edgecolor = 'black', bins=bins, range = [xmin,xmax], weights = weights, log = log, cumulative = cumul)\n",
    "    plt.title(title)\n",
    "    plt.xlabel(xlabel); plt.ylabel('Frequency') \n",
    "    return\n",
    "\n",
    "# location map, reimplemention in Python of GSLIB locmap with MatPlotLib methods\n",
    "def locmap(df,xcol,ycol,vcol,xmin,xmax,ymin,ymax,vmin,vmax,title,xlabel,ylabel,vlabel,cmap,fig_name):\n",
    "    ixy = 0 \n",
    "    plt.figure(figsize=(8,6))    \n",
    "    im = plt.scatter(df[xcol],df[ycol],s=None, c=df[vcol], marker=None, cmap=cmap, norm=None, vmin=vmin, vmax=vmax, alpha=0.8, linewidths=0.8, verts=None, edgecolors=\"black\")\n",
    "    plt.title(title)\n",
    "    plt.xlim(xmin,xmax)\n",
    "    plt.ylim(ymin,ymax)    \n",
    "    plt.xlabel(xlabel)\n",
    "    plt.ylabel(ylabel)\n",
    "    cbar = plt.colorbar(im, orientation = 'vertical',ticks=np.linspace(vmin,vmax,10))\n",
    "    cbar.set_label(vlabel, rotation=270, labelpad=20)\n",
    "    plt.savefig(fig_name + '.' + image_type,dpi=dpi)\n",
    "    plt.show()\n",
    "    return im\n",
    "\n",
    "# location map, reimplemention in Python of GSLIB locmap with MatPlotLib methods (version for subplots)\n",
    "def locmap_st(df,xcol,ycol,vcol,xmin,xmax,ymin,ymax,vmin,vmax,title,xlabel,ylabel,vlabel,cmap):\n",
    "    ixy = 0   \n",
    "    im = plt.scatter(df[xcol],df[ycol],s=None, c=df[vcol], marker=None, cmap=cmap, norm=None, vmin=vmin, vmax=vmax, alpha=0.8, linewidths=0.8, verts=None, edgecolors=\"black\")\n",
    "    plt.title(title)\n",
    "    plt.xlim(xmin,xmax)\n",
    "    plt.ylim(ymin,ymax)    \n",
    "    plt.xlabel(xlabel)\n",
    "    plt.ylabel(ylabel)\n",
    "    cbar = plt.colorbar(im, orientation = 'vertical',ticks=np.linspace(vmin,vmax,10))\n",
    "    cbar.set_label(vlabel, rotation=270, labelpad=20)\n",
    "    return im           \n",
    "\n",
    "# pixel plot, reimplemention in Python of GSLIB pixelplt with MatPlotLib methods\n",
    "def pixelplt(array,xmin,xmax,ymin,ymax,step,vmin,vmax,title,xlabel,ylabel,vlabel,cmap,fig_name):\n",
    "    xx, yy = np.meshgrid(np.arange(xmin, xmax, step),np.arange(ymax, ymin, -1*step))\n",
    "    plt.figure(figsize=(8,6))\n",
    "    im = plt.contourf(xx,yy,array,cmap=cmap,vmin=vmin,vmax=vmax,levels=np.linspace(vmin,vmax,100))\n",
    "    plt.title(title)\n",
    "    plt.xlabel(xlabel)\n",
    "    plt.ylabel(ylabel)\n",
    "    cbar = plt.colorbar(im,orientation = 'vertical',ticks=np.linspace(vmin,vmax,10))\n",
    "    cbar.set_label(vlabel, rotation=270, labelpad=20)\n",
    "    plt.savefig(fig_name + '.' + image_type,dpi=dpi)\n",
    "    plt.show()\n",
    "    return im\n",
    "\n",
    "# pixel plot, reimplemention in Python of GSLIB pixelplt with MatPlotLib methods(version for subplots)\n",
    "def pixelplt_st(array,xmin,xmax,ymin,ymax,step,vmin,vmax,title,xlabel,ylabel,vlabel,cmap):\n",
    "    xx, yy = np.meshgrid(np.arange(xmin, xmax, step),np.arange(ymax, ymin, -1*step))\n",
    "    ixy = 0 \n",
    "    x = [];y = []; v = [] # use dummy since scatter plot controls legend min and max appropriately and contour does not!\n",
    "    cs = plt.contourf(xx,yy,array,cmap=cmap,vmin=vmin,vmax=vmax,levels = np.linspace(vmin,vmax,100))\n",
    "    im = plt.scatter(x,y,s=None, c=v, marker=None,cmap=cmap, vmin=vmin, vmax=vmax, alpha=0.8, linewidths=0.8, verts=None, edgecolors=\"black\")\n",
    "    plt.title(title)\n",
    "    plt.xlabel(xlabel)\n",
    "    plt.ylabel(ylabel)\n",
    "    plt.clim(vmin,vmax)\n",
    "    cbar = plt.colorbar(im, orientation = 'vertical')\n",
    "    cbar.set_label(vlabel, rotation=270, labelpad=20)\n",
    "    return cs\n",
    "\n",
    "# pixel plot and location map, reimplementation in Python of a GSLIB MOD with MatPlotLib methods\n",
    "def locpix(array,xmin,xmax,ymin,ymax,step,vmin,vmax,df,xcol,ycol,vcol,title,xlabel,ylabel,vlabel,cmap,fig_name):\n",
    "    xx, yy = np.meshgrid(np.arange(xmin, xmax, step),np.arange(ymax, ymin, -1*step))\n",
    "    ixy = 0 \n",
    "    plt.figure(figsize=(8,6))\n",
    "    cs = plt.contourf(xx, yy, array, cmap=cmap,vmin=vmin, vmax=vmax,levels = np.linspace(vmin,vmax,100))\n",
    "    im = plt.scatter(df[xcol],df[ycol],s=None, c=df[vcol], marker=None, cmap=cmap, vmin=vmin, vmax=vmax, alpha=0.8, linewidths=0.8, verts=None, edgecolors=\"black\")\n",
    "    plt.title(title)\n",
    "    plt.xlabel(xlabel)\n",
    "    plt.ylabel(ylabel)\n",
    "    plt.xlim(xmin,xmax)\n",
    "    plt.ylim(ymin,ymax)  \n",
    "    cbar = plt.colorbar(orientation = 'vertical')\n",
    "    cbar.set_label(vlabel, rotation=270, labelpad=20)\n",
    "    plt.savefig(fig_name + '.' + image_type,dpi=dpi)\n",
    "    plt.show()\n",
    "    return cs\n",
    "\n",
    "# pixel plot and location map, reimplementation in Python of a GSLIB MOD with MatPlotLib methods(version for subplots)\n",
    "def locpix_st(array,xmin,xmax,ymin,ymax,step,vmin,vmax,df,xcol,ycol,vcol,title,xlabel,ylabel,vlabel,cmap):\n",
    "    xx, yy = np.meshgrid(np.arange(xmin, xmax, step),np.arange(ymax, ymin, -1*step))\n",
    "    ixy = 0 \n",
    "    cs = plt.contourf(xx, yy, array, cmap=cmap,vmin=vmin, vmax=vmax,levels = np.linspace(vmin,vmax,100))\n",
    "    im = plt.scatter(df[xcol],df[ycol],s=None, c=df[vcol], marker=None, cmap=cmap, vmin=vmin, vmax=vmax, alpha=0.8, linewidths=0.8, verts=None, edgecolors=\"black\")\n",
    "    plt.title(title)\n",
    "    plt.xlabel(xlabel)  \n",
    "    plt.ylabel(ylabel)\n",
    "    plt.xlim(xmin,xmax)\n",
    "    plt.ylim(ymin,ymax)\n",
    "    cbar = plt.colorbar(orientation = 'vertical')\n",
    "    cbar.set_label(vlabel, rotation=270, labelpad=20)\n",
    "\n",
    "# affine distribution correction reimplemented in Python with numpy methods \n",
    "def affine(array,tmean,tstdev): \n",
    "    mean = np.average(array)\n",
    "    stdev = np.std(array)  \n",
    "    array = (tstdev/stdev)*(array - mean) + tmean\n",
    "    return(array)   \n",
    "\n",
    "def make_variogram(nug,nst,it1,cc1,azi1,hmaj1,hmin1,it2=1,cc2=0,azi2=0,hmaj2=0,hmin2=0):\n",
    "    if cc2 == 0:\n",
    "        nst = 1\n",
    "    var = dict([('nug', nug), ('nst', nst), ('it1', it1),('cc1', cc1),('azi1', azi1),('hmaj1', hmaj1), ('hmin1', hmin1), \n",
    "      ('it2', it2),('cc2', cc2),('azi2', azi2),('hmaj2', hmaj2), ('hmin2', hmin2)])\n",
    "    if nug + cc1 + cc2 != 1:\n",
    "        print('\\x1b[0;30;41m make_variogram Warning: sill does not sum to 1.0, do not use in simulation \\x1b[0m')\n",
    "    if cc1 < 0 or cc2 < 0 or nug < 0 or hmaj1 < 0 or hmaj2 < 0 or hmin1 < 0 or hmin2 < 0:\n",
    "        print('\\x1b[0;30;41m make_variogram Warning: contributions and ranges must be all positive \\x1b[0m')\n",
    "    if hmaj1 < hmin1 or hmaj2 < hmin2:\n",
    "        print('\\x1b[0;30;41m make_variogram Warning: major range should be greater than minor range \\x1b[0m')\n",
    "    return var    \n",
    "\n",
    "# sequential Gaussian simulation, 2D unconditional wrapper for sgsim from GSLIB (.exe must be in working directory)\n",
    "def GSLIB_sgsim_2d_uncond(nreal,nx,ny,hsiz,seed,var,output_file):\n",
    "    import os\n",
    "    import numpy as np \n",
    "    \n",
    "    nug = var['nug']\n",
    "    nst = var['nst']; it1 = var['it1']; cc1 = var['cc1']; azi1 = var['azi1']; hmaj1 = var['hmaj1']; hmin1 = var['hmin1'] \n",
    "    it2 = var['it2']; cc2 = var['cc2']; azi2 = var['azi2']; hmaj2 = var['hmaj2']; hmin2 = var['hmin2']     \n",
    "    max_range = max(hmaj1,hmaj2) \n",
    "    hmn = hsiz * 0.5   \n",
    "    hctab = int(max_range/hsiz)*2 + 1\n",
    "\n",
    "    sim_array = np.random.rand(nx,ny)\n",
    "\n",
    "    file = open(\"sgsim.par\", \"w\")\n",
    "    file.write(\"              Parameters for SGSIM                                         \\n\")\n",
    "    file.write(\"              ********************                                         \\n\")\n",
    "    file.write(\"                                                                           \\n\")\n",
    "    file.write(\"START OF PARAMETER:                                                        \\n\")\n",
    "    file.write(\"none                          -file with data                              \\n\")\n",
    "    file.write(\"1  2  0  3  5  0              -  columns for X,Y,Z,vr,wt,sec.var.          \\n\")\n",
    "    file.write(\"-1.0e21 1.0e21                -  trimming limits                           \\n\")\n",
    "    file.write(\"0                             -transform the data (0=no, 1=yes)            \\n\")\n",
    "    file.write(\"none.trn                      -  file for output trans table               \\n\")\n",
    "    file.write(\"1                             -  consider ref. dist (0=no, 1=yes)          \\n\")\n",
    "    file.write(\"none.dat                      -  file with ref. dist distribution          \\n\")\n",
    "    file.write(\"1  0                          -  columns for vr and wt                     \\n\")\n",
    "    file.write(\"-4.0    4.0                   -  zmin,zmax(tail extrapolation)             \\n\")\n",
    "    file.write(\"1      -4.0                   -  lower tail option, parameter              \\n\")\n",
    "    file.write(\"1       4.0                   -  upper tail option, parameter              \\n\")\n",
    "    file.write(\"0                             -debugging level: 0,1,2,3                    \\n\")\n",
    "    file.write(\"nonw.dbg                      -file for debugging output                   \\n\")\n",
    "    file.write(str(output_file) + \"           -file for simulation output                  \\n\")\n",
    "    file.write(str(nreal) + \"                 -number of realizations to generate          \\n\")\n",
    "    file.write(str(nx) + \" \" + str(hmn) + \" \" + str(hsiz) + \"                              \\n\")\n",
    "    file.write(str(ny) + \" \" + str(hmn) + \" \" + str(hsiz) + \"                              \\n\")\n",
    "    file.write(\"1 0.0 1.0                     - nz zmn zsiz                                \\n\")\n",
    "    file.write(str(seed) + \"                  -random number seed                          \\n\")\n",
    "    file.write(\"0     8                       -min and max original data for sim           \\n\")\n",
    "    file.write(\"12                            -number of simulated nodes to use            \\n\")\n",
    "    file.write(\"0                             -assign data to nodes (0=no, 1=yes)          \\n\")\n",
    "    file.write(\"1     3                       -multiple grid search (0=no, 1=yes),num      \\n\")\n",
    "    file.write(\"0                             -maximum data per octant (0=not used)        \\n\")\n",
    "    file.write(str(max_range) + \" \" + str(max_range) + \" 1.0 -maximum search  (hmax,hmin,vert) \\n\")\n",
    "    file.write(str(azi1) + \"   0.0   0.0       -angles for search ellipsoid                 \\n\")\n",
    "    file.write(str(hctab) + \" \" + str(hctab) + \" 1 -size of covariance lookup table        \\n\")\n",
    "    file.write(\"0     0.60   1.0              -ktype: 0=SK,1=OK,2=LVM,3=EXDR,4=COLC        \\n\")\n",
    "    file.write(\"none.dat                      -  file with LVM, EXDR, or COLC variable     \\n\")\n",
    "    file.write(\"4                             -  column for secondary variable             \\n\")\n",
    "    file.write(str(nst) + \" \" + str(nug) + \"  -nst, nugget effect                          \\n\")\n",
    "    file.write(str(it1) + \" \" + str(cc1) + \" \" +str(azi1) + \" 0.0 0.0 -it,cc,ang1,ang2,ang3\\n\")\n",
    "    file.write(\" \" + str(hmaj1) + \" \" + str(hmin1) + \" 1.0 - a_hmax, a_hmin, a_vert        \\n\")\n",
    "    file.write(str(it2) + \" \" + str(cc2) + \" \" +str(azi2) + \" 0.0 0.0 -it,cc,ang1,ang2,ang3\\n\")\n",
    "    file.write(\" \" + str(hmaj2) + \" \" + str(hmin2) + \" 1.0 - a_hmax, a_hmin, a_vert        \\n\")  \n",
    "    file.close()\n",
    "\n",
    "    os.system('\"sgsim.exe sgsim.par\"')       \n",
    "    sim_array = GSLIB2ndarray(output_file,0,nx,ny)         \n",
    "    return(sim_array[0])\n",
    "\n",
    "# extract regular spaced samples from a model   \n",
    "def regular_sample(array,xmin,xmax,ymin,ymax,step,mx,my,name):\n",
    "    x = []; y = []; v = []; iix = 0; iiy = 0;\n",
    "    xx, yy = np.meshgrid(np.arange(xmin, xmax, step),np.arange(ymax, ymin, -1*step))\n",
    "    iiy = 0\n",
    "    for iy in range(0,ny):\n",
    "        if iiy >= my:\n",
    "            iix = 0\n",
    "            for ix in range(0,nx):\n",
    "                if iix >= mx:\n",
    "                    x.append(xx[ix,iy]);y.append(yy[ix,iy]); v.append(array[ix,iy])\n",
    "                    iix = 0; iiy = 0\n",
    "                iix = iix + 1\n",
    "        iiy = iiy + 1\n",
    "    df = pd.DataFrame(np.c_[x,y,v],columns=['X', 'Y', name])\n",
    "    return(df)\n",
    "\n",
    "def random_sample(array,xmin,xmax,ymin,ymax,step,nsamp,name):\n",
    "    import random as rand\n",
    "    x = []; y = []; v = []; iix = 0; iiy = 0;\n",
    "    xx, yy = np.meshgrid(np.arange(xmin, xmax, step),np.arange(ymax-1, ymin-1, -1*step))\n",
    "    ny = xx.shape[0]\n",
    "    nx = xx.shape[1]\n",
    "    sample_index = rand.sample(range((nx)*(ny)), nsamp)\n",
    "    for isamp in range(0,nsamp):\n",
    "        iy = int(sample_index[isamp]/ny)\n",
    "        ix = sample_index[isamp] - iy*nx\n",
    "        x.append(xx[iy,ix])\n",
    "        y.append(yy[iy,ix])\n",
    "        v.append(array[iy,ix])\n",
    "    df = pd.DataFrame(np.c_[x,y,v],columns=['X', 'Y', name])\n",
    "    return(df) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These are a variety of GSLIB subfunctions required by the GSLIB functions that I have converted to Python.  I will continue to convert the subfunctions and include them as needed by the GSLIB functions that I convert. All are available with geostatspy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import numpy as np\n",
    "import numpy.linalg as linalg\n",
    "from numba import jit\n",
    "\n",
    "def dlocate(xx,iis,iie,x):\n",
    "    from bisect import bisect\n",
    "    n = len(xx)\n",
    "    if iie <= iis:\n",
    "        iis = 0; ie = n-1\n",
    "    array = xx[iis:iie-1]  # this is accounting for swith to 0,...,n-1 index\n",
    "    j = bisect(array,x)\n",
    "    return j\n",
    "\n",
    "def dsortem(ib,ie,a,iperm,b=0,c=0,d=0,e=0,f=0,g=0,h=0):\n",
    "    a = a[ib:ie]\n",
    "    inds = a.argsort()\n",
    "    a = np.copy(a[inds]) # deepcopy forces pass to outside scope\n",
    "    if(iperm == 1):\n",
    "        return a\n",
    "    b_slice = b[ib:ie]\n",
    "    b = b_slice[inds]    \n",
    "    if iperm == 2:\n",
    "        return a,b\n",
    "    c_slice = c[ib:ie]\n",
    "    c = c_slice[inds]    \n",
    "    if iperm == 3:\n",
    "        return a, b, c\n",
    "    d_slice = d[ib:ie]\n",
    "    d = d_slice[inds]    \n",
    "    if iperm == 4:\n",
    "        return a, b, c, d\n",
    "    e_slice = e[ib:ie]\n",
    "    e = e_slice[inds]    \n",
    "    if iperm == 5:\n",
    "        return a, b, c, d, e \n",
    "    f_slice = f[ib:ie]\n",
    "    f = f_slice[inds]\n",
    "    if iperm == 6:\n",
    "        return a, b, c, d, e, f \n",
    "    g_slice = g[ib:ie]\n",
    "    g = g_slice[inds]\n",
    "    if iperm == 7:\n",
    "        return a, b, c, d, e, f, h     \n",
    "    h_slice = h[ib:ie]\n",
    "    h = h_slice[inds]\n",
    "    return a, b, c, d, e, f, h\n",
    "\n",
    "def backtr_value(vrgs,vr,vrg,zmin,zmax,ltail,ltpar,utail,utpar):   \n",
    "    EPSLON=1.0e-20\n",
    "    nt = len(vr) # number of data to transform \n",
    "# Value in the lower tail?    1=linear, 2=power, (3 and 4 are invalid):\n",
    "    if vrgs <= vrg[0]:\n",
    "        backtr[id] = vr[0]\n",
    "        cdflo  = gcum(vrg[0])\n",
    "        cdfbt  = gcum(vrgs)\n",
    "        if ltail == 1:\n",
    "            backtr = powint(0.0,cdflo,zmin,vr[0],cdfbt,1.0)\n",
    "        elif ltail == 2:\n",
    "            cpow   = 1.0 / ltpar\n",
    "            backtr = powint(0.0,cdflo,zmin,vr[0],cdfbt,cpow)\n",
    "# Value in the upper tail?     1=linear, 2=power, 4=hyperbolic:\n",
    "    elif vrgs >= vrg[nt-1]:\n",
    "        backtr = vr[nt-1]\n",
    "        cdfhi  = gcum(vrg[nt-1])\n",
    "        cdfbt  = gcum(vrgs)\n",
    "        if utail == 1:\n",
    "            backtr[id] = powint(cdfhi,1.0,vr[nt-1],zmax,cdfbt,1.0)\n",
    "        elif utail == 2: \n",
    "            cpow   = 1.0 / utpar\n",
    "            backtr[id] = powint(cdfhi,1.0,vr[nt-1],zmax,cdfbt,cpow)\n",
    "        elif utail == 4:\n",
    "            plambda = (vr[nt-1]**utpar)*(1.0-gcum(vrg[nt-1]))\n",
    "            backtr[id] = (plambda/(1.0-gcum(vrgs)))**(1.0/utpar)\n",
    "    else:\n",
    "# Value within the transformation table:\n",
    "        j = locate(vrg,1,nt,vrgs)\n",
    "        j = max(min((nt-2),j),1)\n",
    "        backtr = powint(vrg[j],vrg[j+1],vr[j],vr[j+1],vrgs,1.0)\n",
    "    return backtr\n",
    "\n",
    "def backtr(df,vcol,vr,vrg,zmin,zmax,ltail,ltpar,utail,utpar):   \n",
    "    EPSLON=1.0e-20\n",
    "    nd = len(df); nt = len(vr) # number of data to transform and number of data in table\n",
    "    backtr = np.zeros(nd)\n",
    "    vrgs = df[vcol].values\n",
    "# Value in the lower tail?    1=linear, 2=power, (3 and 4 are invalid):\n",
    "    for id in range(0,nd):\n",
    "        if vrgs[id] <= vrg[0]:\n",
    "            backtr[id] = vr[0]\n",
    "            cdflo  = gcum(vrg[0])\n",
    "            cdfbt  = gcum(vrgs[id])\n",
    "            if ltail == 1:\n",
    "                backtr[id] = powint(0.0,cdflo,zmin,vr[0],cdfbt,1.0)\n",
    "            elif ltail == 2:\n",
    "                cpow   = 1.0 / ltpar\n",
    "                backtr[id] = powint(0.0,cdflo,zmin,vr[0],cdfbt,cpow)\n",
    "# Value in the upper tail?     1=linear, 2=power, 4=hyperbolic:\n",
    "        elif vrgs[id] >= vrg[nt-1]:\n",
    "            backtr[id] = vr[nt-1]\n",
    "            cdfhi  = gcum(vrg[nt-1])\n",
    "            cdfbt  = gcum(vrgs[id])\n",
    "            if utail == 1:\n",
    "                backtr[id] = powint(cdfhi,1.0,vr[nt-1],zmax,cdfbt,1.0)\n",
    "            elif utail == 2: \n",
    "                cpow   = 1.0 / utpar\n",
    "                backtr[id] = powint(cdfhi,1.0,vr[nt-1],zmax,cdfbt,cpow)\n",
    "            elif utail == 4:\n",
    "                plambda = (vr[nt-1]**utpar)*(1.0-gcum(vrg[nt-1]))\n",
    "                backtr[id] = (plambda/(1.0-gcum(vrgs)))**(1.0/utpar)\n",
    "        else:\n",
    "# Value within the transformation table:\n",
    "            j = locate(vrg,1,nt,vrgs[id])\n",
    "            j = max(min((nt-2),j),1)\n",
    "            backtr[id] = powint(vrg[j],vrg[j+1],vr[j],vr[j+1],vrgs[id],1.0)\n",
    "    return backtr\n",
    "\n",
    "def gauinv(p):\n",
    "    lim = 1.0e-10; p0 = -0.322232431088; p1 = -1.0; p2 = -0.342242088547\n",
    "    p3 = -0.0204231210245; p4 = -0.0000453642210148; q0 = 0.0993484626060\n",
    "    q1 = 0.588581570495; q2 = 0.531103462366; q3 = 0.103537752850; q4 = 0.0038560700634\n",
    "\n",
    "# Check for an error situation:\n",
    "    if p < lim:\n",
    "        xp = -1.0e10\n",
    "        return xp\n",
    "    if p > (1.0-lim):\n",
    "        xp =  1.0e10\n",
    "        return xp    \n",
    "\n",
    "# Get k for an error situation:\n",
    "    pp = p\n",
    "    if p > 0.5: pp = 1 - pp\n",
    "    xp   = 0.0\n",
    "    if p == 0.5: \n",
    "        return xp\n",
    "\n",
    "# Approximate the function:\n",
    "    y  = np.sqrt(np.log(1.0/(pp*pp)))\n",
    "    xp = float(y + ((((y*p4+p3)*y+p2)*y+p1)*y+p0) /\n",
    "            ((((y*q4+q3)*y+q2)*y+q1)*y+q0) )\n",
    "    if float(p) == float(pp): \n",
    "        xp = -xp\n",
    "    return xp\n",
    "\n",
    "def gcum(x):\n",
    "    z = x\n",
    "    if z < 0:  \n",
    "        z = -z\n",
    "    t= 1./(1.+ 0.2316419*z)\n",
    "    gcum = t*(0.31938153   + t*(-0.356563782 + t*(1.781477937 +\n",
    "           t*(-1.821255978 + t*1.330274429))))\n",
    "    e2= 0.0\n",
    "    \n",
    "# standard deviations out gets treated as infinity:\n",
    "    if z <= 6: \n",
    "        e2 = np.exp(-z*z/2.0)*0.3989422803\n",
    "    gcum = 1.0- e2 * gcum\n",
    "    if x >= 0.0: \n",
    "        return gcum\n",
    "    gcum = 1.0 - gcum\n",
    "    return gcum\n",
    "\n",
    "def dpowint(xlow,xhigh,ylow,yhigh,xval,pwr):\n",
    "    EPSLON = 1.0e-20\n",
    "    if (xhigh-xlow) < EPSLON:\n",
    "        dpowint = (yhigh+ylow)/2.0\n",
    "    else:\n",
    "        dpowint = ylow + (yhigh-ylow)*(((xval-xlow)/(xhigh-xlow))**pwr)\n",
    "    return dpowint\n",
    "\n",
    "#@jit(nopython=True) # all NumPy array operations included in this function for precompile with NumBa\n",
    "def setup_rotmat(c0,nst,it,cc,ang):\n",
    "    DTOR=3.14159265/180.0; EPSLON=0.000000; PI=3.141593\n",
    "# The first time around, re-initialize the cosine matrix for the\n",
    "# variogram structures:\n",
    "    rotmat = np.zeros((4,nst))\n",
    "    maxcov = c0\n",
    "    for js in range(0,nst):\n",
    "        azmuth = (90.0-ang[js])*DTOR\n",
    "        rotmat[0,js] =  math.cos(azmuth)\n",
    "        rotmat[1,js] =  math.sin(azmuth)\n",
    "        rotmat[2,js] = -1*math.sin(azmuth)\n",
    "        rotmat[3,js] =  math.cos(azmuth)\n",
    "        if it[js] == 4:\n",
    "            maxcov = maxcov + 9999.9\n",
    "        else:\n",
    "            maxcov = maxcov + cc[js]\n",
    "    return rotmat, maxcov\n",
    "     \n",
    "@jit(nopython=True) # all NumPy array operations included in this function for precompile with NumBa\n",
    "def cova2(x1,y1,x2,y2,nst,c0,PMX,cc,aa,it,ang,anis,rotmat,maxcov):\n",
    "    DTOR=3.14159265/180.0; EPSLON=0.0000001; PI=3.141593\n",
    "                      \n",
    "# Check for very small distance:\n",
    "    dx = x2-x1\n",
    "    dy = y2-y1\n",
    "#    print(dx,dy)\n",
    "    if (dx*dx+dy*dy) < EPSLON:\n",
    "        cova2 = maxcov\n",
    "        return cova2\n",
    "\n",
    "# Non-zero distance, loop over all the structures:\n",
    "    cova2 = 0.0\n",
    "    for js in range(0,nst):\n",
    "#        print(js)\n",
    "#        print(rotmat)\n",
    "# Compute the appropriate structural distance:\n",
    "        dx1 = (dx*rotmat[0,js] + dy*rotmat[1,js])\n",
    "        dy1 = (dx*rotmat[2,js] + dy*rotmat[3,js])/anis[js]\n",
    "        h   = math.sqrt(max((dx1*dx1+dy1*dy1),0.0))\n",
    "        if it[js] == 1:\n",
    "\n",
    "# Spherical model:\n",
    "            hr = h/aa[js]\n",
    "            if hr < 1.0: \n",
    "                cova2 = cova2 + cc[js]*(1.-hr*(1.5-.5*hr*hr))\n",
    "            elif it[js] == 2:\n",
    "                \n",
    "# Exponential model:\n",
    "                cova2 = cova2 + cc[js]*np.exp(-3.0*h/aa[js])\n",
    "            elif it[js] == 3:\n",
    "\n",
    "# Gaussian model:\n",
    "                hh=-3.0*(h*h)/(aa[js]*aa[js])\n",
    "                cova2 = cova2 +cc[js]*np.exp(hh)\n",
    "            elif it[js] == 4:\n",
    "\n",
    "# Power model:\n",
    "                cov1  = PMX - cc[js]*(h**aa[js])\n",
    "                cova2 = cova2 + cov1      \n",
    "    return cova2\n",
    "\n",
    "def sqdist2(x1,y1,x2,y2,ist,rotmat,anis):\n",
    "# Compute component distance vectors and the squared distance:\n",
    "    dx = x1 - x2\n",
    "    dy = y1 - y2\n",
    "    dx1 = (dx*rotmat[0,ist] + dy*rotmat[1,ist])\n",
    "    dy1 = (dx*rotmat[2,ist] + dy*rotmat[3,ist])/anis[ist]\n",
    "    sqdist = (dx1*dx1+dy1*dy1)  \n",
    "    return sqdist\n",
    "\n",
    "def ksol_numpy(neq,a,r):    # using Numpy methods\n",
    "    a = a[0:neq*neq]             # trim the array\n",
    "    a = np.reshape(a,(neq,neq))  # reshape to 2D\n",
    "    ainv = linalg.inv(a)         # invert matrix\n",
    "    r = r[0:neq]                 # trim the array\n",
    "    s = np.matmul(ainv,r)        # matrix multiplication\n",
    "    return s\n",
    "\n",
    "def setrot(ang1,ang2,sang1,anis1,anis2,sanis1,nst,MAXROT):\n",
    "    DEG2RAD = 3.141592654/180.0; EPSLON=1.e-20\n",
    "    rotmat = np.zeros((MAXROT+1,3,3))\n",
    "    if ang1 >= 0.0 and ang1 < 270.0:\n",
    "        alpha = (90.0   - ang1) * DEG2RAD\n",
    "    else:\n",
    "        alpha = (450.0  - ang1) * DEG2RAD\n",
    "# Get the required sines and cosines:\n",
    "    sina  = math.sin(alpha)\n",
    "    cosa  = math.cos(alpha)\n",
    "# Construct the rotation matrix in the required memory:\n",
    "    afac1 = 1.0 / (max(anis1,EPSLON))\n",
    "    rotmat[0,1,1] = cosa\n",
    "    rotmat[0,1,2] = sina\n",
    "    rotmat[0,2,1] = afac1*(-sina)\n",
    "    rotmat[0,2,2] = afac1*(cosa)\n",
    "# 2nd structure if present    \n",
    "    if nst > 1:\n",
    "        if ang2 >= 0.0 and ang2 < 270.0:\n",
    "            alpha = (90.0   - ang2) * DEG2RAD\n",
    "        else:\n",
    "            alpha = (450.0  - ang2) * DEG2RAD\n",
    "# Get the required sines and cosines:\n",
    "        sina  = math.sin(alpha)\n",
    "        cosa  = math.cos(alpha)\n",
    "# Construct the rotation matrix in the required memory:\n",
    "        afac2 = 1.0 / (max(anis2,EPSLON))\n",
    "        rotmat[1,1,1] = cosa\n",
    "        rotmat[1,1,2] = sina\n",
    "        rotmat[1,2,1] = afac1*(-sina)\n",
    "        rotmat[1,2,2] = afac1*(cosa)\n",
    "# search rotation\n",
    "    if sang1 >= 0.0 and sang1 < 270.0:\n",
    "        alpha = (90.0   - sang1) * DEG2RAD\n",
    "    else:\n",
    "        alpha = (450.0  - sang1) * DEG2RAD\n",
    "# Get the required sines and cosines:\n",
    "    sina  = math.sin(alpha)\n",
    "    cosa  = math.cos(alpha)\n",
    "# Construct the rotation matrix in the required memory:\n",
    "    afac1 = 1.0 / (max(sanis1,EPSLON))\n",
    "    rotmat[MAXROT,1,1] = cosa\n",
    "    rotmat[MAXROT,1,2] = sina\n",
    "    rotmat[MAXROT,2,1] = afac1*(-sina)\n",
    "    rotmat[MAXROT,2,2] = afac1*(cosa)\n",
    "# Return to calling program:\n",
    "    return rotmat\n",
    "\n",
    "def sqdist(x1,y1,z1,x2,y2,z2,ind,rotmat):\n",
    "# Compute component distance vectors and the squared distance:\n",
    "    dx = x1 - x2\n",
    "    dy = y1 - y2\n",
    "    dz = 0.0\n",
    "    sqdist = 0.0\n",
    "    for i in range(0,2):\n",
    "        cont   = rotmat[ind,i,1] * dx + rotmat[ind,i,2] * dy\n",
    "        sqdist = sqdist + cont * cont\n",
    "    return sqdist\n",
    "\n",
    "def ctable(MAXNOD,MAXCXY,MAXCTX,MAXCTY,MAXXYZ,isrot,nx,ny,nst,c0,cc,aa,it,ang,anis,global_rotmat,radsqd):\n",
    "\n",
    "    \n",
    "# Declare constants\n",
    "    TINY = 1.0e-10\n",
    "    PMX = 9999.9\n",
    "    MAXROT=2\n",
    "# Size of the look-up table:\n",
    "    tmp = np.zeros(MAXXYZ)\n",
    "    MAXORD = MAXXYZ\n",
    "    if (nx*ny)  < MAXCXY: \n",
    "        MAXORD = MAXCXY\n",
    "     \n",
    "    order = np.zeros(MAXORD)\n",
    "    nctx = int(min(((MAXCTX-1)/2),(nx-1)))\n",
    "    ncty = int(min(((MAXCTY-1)/2),(ny-1)))\n",
    "    \n",
    "#    print('CTable check')\n",
    "#    print('nctx ' + str(nctx) + ', ncty ' + str(ncty))\n",
    "    \n",
    "    ixnode = np.zeros(MAXXYZ)\n",
    "    iynode = np.zeros(MAXXYZ)\n",
    "    covtab = np.zeros((MAXCTX,MAXCTY))\n",
    "# Initialize the covariance subroutine and cbb at the same time:\n",
    "    rotmat, maxcov = setup_rotmat(c0,nst,it,cc,ang)\n",
    "    cbb = cova2(0.0,0.0,0.0,0.0,nst,c0,PMX,cc,aa,it,ang,anis,rotmat,maxcov)\n",
    "\n",
    "# Now, set up the table and keep track of the node offsets that are\n",
    "# within the search radius:\n",
    "    nlooku = -1 # adjusted for 0 origin\n",
    "    for i in range(-nctx,nctx+1):   # cover entire range   \n",
    "        xx = i * xsiz\n",
    "        ic = nctx + i\n",
    "        for j in range(-ncty,ncty+1):   # cover entire range      \n",
    "            yy = j * ysiz\n",
    "            jc = ncty + j\n",
    "\n",
    "            covtab[ic,jc] = cova2(0.0,0.0,xx,yy,nst,c0,PMX,cc,aa,it,ang,anis,rotmat,maxcov)\n",
    "#            print('cov table offset'); print(xx,yy); print(covtab[ic,jc])\n",
    "            hsqd = sqdist(0.0,0.0,0.0,xx,yy,0.0,MAXROT,global_rotmat)\n",
    "            if hsqd <= radsqd: \n",
    "                nlooku = nlooku + 1\n",
    "\n",
    "# We want to search by closest variogram distance (and use the\n",
    "# anisotropic Euclidean distance to break ties:\n",
    "                tmp[nlooku]   = - (covtab[ic,jc] - TINY*hsqd)\n",
    "                order[nlooku] = (jc)*MAXCTX+ic\n",
    "#    print('populated presort'); print(tmp,order) \n",
    "# Finished setting up the look-up table, now order the nodes such\n",
    "# that the closest ones, according to variogram distance, are searched\n",
    "# first. Note: the \"loc\" array is used because I didn't want to make\n",
    "# special allowance for 2 byte integers in the sorting subroutine:\n",
    "                              \n",
    "    nlooku = nlooku + 1\n",
    "#    print('nlooku' + str(nlooku)); print('MAXCTX' + str(MAXCTX))\n",
    "    tmp, order = dsortem(0,nlooku,tmp,2,b=order)\n",
    "#    print('populated postsort'); print(tmp,order)\n",
    "    for il in range(0,nlooku):                                    \n",
    "        loc = int(order[il])\n",
    "        iy  = int((loc-0)/MAXCTX)\n",
    "        ix  = loc - (iy-0)*MAXCTX\n",
    "        iynode[il] = int(iy)\n",
    "        ixnode[il] = int(ix)\n",
    "#    print('populated ix, iy node list'); print(ixnode, iynode)\n",
    "                                   \n",
    "    return covtab,tmp,order,ixnode,iynode,nlooku,nctx,ncty\n",
    "    \n",
    "def srchnd(ix,iy,sim,noct,nodmax,ixnode,iynode,nlooku,nctx,ncty,UNEST):\n",
    "\n",
    "# Consider all the nearby nodes until enough have been found:\n",
    "    ncnode = 0; \n",
    "    icnode = np.zeros(nodmax,dtype=int); icnode.fill(-1) \n",
    "    cnodev = np.zeros(nodmax);cnodex = np.zeros(nodmax); cnodey = np.zeros(nodmax);\n",
    "    \n",
    "#    print('Node search at '); print(ix,iy)\n",
    "#    print('nlooku'); print(nlooku)\n",
    "    if noct > 0:\n",
    "        ninoct = np.zeros(8)\n",
    "    for il in range(0,nlooku):   \n",
    "        if ncnode == nodmax: return ncnode, icnode, cnodev, cnodex, cnodey\n",
    "        i = ix + (int(ixnode[il])-nctx)\n",
    "        j = iy + (int(iynode[il])-ncty)\n",
    "        #print('i,j'); print(i,j)\n",
    "        if i < 0 or j < 0: continue\n",
    "        if i >= nx or j >= ny: continue\n",
    "        ind = i + (j-1)*nx \n",
    "#        print('ind'); print(ind)\n",
    "        if sim[ind] > UNEST:\n",
    "\n",
    "# Check the number of data already taken from this octant:\n",
    "            if noct > 0: \n",
    "                idx = ix - i\n",
    "                idy = iy - j\n",
    "                iq = 8\n",
    "                if idx <= 0 and idy > 0: iq = 5\n",
    "                if idx > 0 and idy >= 0: iq = 6\n",
    "                if idx < 0 and idy <= 0: iq = 7\n",
    "                ninoct[iq] = ninoct[iq] + 1\n",
    "                if ninoct[iq] > noct: continue\n",
    "            icnode[ncnode] = il\n",
    "            cnodex[ncnode] = xmn + (i)*xsiz # adjust for 0 origin\n",
    "            cnodey[ncnode] = ymn + (j)*ysiz\n",
    "            cnodev[ncnode] = sim[ind]  \n",
    "            ncnode = ncnode + 1  # moved to account for origin 0\n",
    "    return ncnode, icnode, cnodev, cnodex, cnodey\n",
    "\n",
    "#@jit(nopython=True) # all NumPy array operations included in this function for precompile with NumBa\n",
    "def cova2(x1,y1,x2,y2,nst,c0,PMX,cc,aa,it,ang,anis,rotmat,maxcov):\n",
    "    DTOR=3.14159265/180.0; EPSLON=0.000001; PI=3.141593\n",
    "                      \n",
    "# Check for very small distance:\n",
    "    dx = x2-x1\n",
    "    dy = y2-y1\n",
    "#    print(dx,dy)\n",
    "    if (dx*dx+dy*dy) < EPSLON:\n",
    "        cova2 = maxcov\n",
    "        return cova2\n",
    "# Non-zero distance, loop over all the structures:\n",
    "    cova2 = 0.0\n",
    "    for js in range(0,nst):\n",
    "#        print(js)\n",
    "#        print(rotmat)\n",
    "# Compute the appropriate structural distance:\n",
    "        dx1 = (dx*rotmat[0,js] + dy*rotmat[1,js])\n",
    "        dy1 = (dx*rotmat[2,js] + dy*rotmat[3,js])/anis[js]\n",
    "        h   = math.sqrt(max((dx1*dx1+dy1*dy1),0.0))\n",
    "        if it[js] == 1:\n",
    "# Spherical model:\n",
    "            hr = h/aa[js]\n",
    "            if hr < 1.0: \n",
    "                cova2 = cova2 + cc[js]*(1.-hr*(1.5-.5*hr*hr))\n",
    "            elif it[js] == 2:             \n",
    "# Exponential model:\n",
    "                cova2 = cova2 + cc[js]*np.exp(-3.0*h/aa[js])\n",
    "            elif it[js] == 3:\n",
    "# Gaussian model:\n",
    "                hh=-3.0*(h*h)/(aa[js]*aa[js])\n",
    "                cova2 = cova2 +cc[js]*np.exp(hh)\n",
    "            elif it[js] == 4:\n",
    "# Power model:\n",
    "                cov1  = PMX - cc[js]*(h**aa[js])\n",
    "                cova2 = cova2 + cov1      \n",
    "    return cova2\n",
    "\n",
    "def krige(ix,iy,xx,yy,lktype,x,y,vr,sec,lvm,close,covtab,nctx,ncty,icnode,ixnode,iynode,cnodev,cnodex,cnodey,nst,c0,PMX,cc,aa,it,ang,anis,rotmat,maxcov,MAXCTX,MAXCTY,MAXKR1,MAXKR2):\n",
    "    \n",
    "#    print('Check ixnode '); print(ixnode); print(iynode)\n",
    "    nclose = len(close)\n",
    "    ncnode = (icnode >= 0).sum() \n",
    "#    print('In kriging, maxcov = ' + str(maxcov))\n",
    "#    print('kriging')\n",
    "#    print('nclose ' + str(nclose) + ', ncnode ' + str(ncnode))\n",
    "    vra = np.zeros(MAXKR1); vrea = np.zeros(MAXKR1)\n",
    "    r = np.zeros(MAXKR1); rr = np.zeros(MAXKR1); s = np.zeros(MAXKR1); a = np.zeros(MAXKR2)\n",
    "    cbb = cova2(0,0,0,0,nst,c0,9999.9,cc,aa,it,ang,anis,rotmat,maxcov) \n",
    "    \n",
    "# Size of the kriging system:\n",
    "    first = False\n",
    "    na    = nclose + ncnode\n",
    "    if lktype == 0: neq = na\n",
    "    if lktype == 1: neq = na + 1\n",
    "    if lktype == 2: neq = na\n",
    "    if lktype == 3: neq = na + 2\n",
    "    if lktype == 4: neq = na + 1\n",
    "\n",
    "# Set up kriging matrices:\n",
    "    iin=-1 # acocunting for 0 origin\n",
    "#    print('krige na' + str(na))\n",
    "    for j in range(0,na):\n",
    "\n",
    "# Sort out the actual location of point \"j\"\n",
    "        if j < nclose: # adjusted for 0 index origin\n",
    "            index  = int(close[j])\n",
    "            x1     = x[index]\n",
    "            y1     = y[index]\n",
    "            vra[j] = vr[index]\n",
    "#            print('kriging data: index = ' + str(index) + ', x,y ' + str(x1) + ',' + str(y1) + ', value = ' + str(vra[j]))\n",
    "            if sec.shape[0] > 1: \n",
    "                vrea[j]= sec[index];\n",
    "            else:\n",
    "                vrea[j] = 0.0 # added this - no effect\n",
    "            if lktype == 2: vra[j] = vra[j] - vrea[j]\n",
    "        else:\n",
    "            \n",
    "# It is a previously simulated node (keep index for table look-up):\n",
    "#            print(j)\n",
    "            index  = j-(nclose) # adjust for 0 index\n",
    "            x1     = cnodex[index]\n",
    "            y1     = cnodey[index]\n",
    "            vra[j] = cnodev[index]\n",
    "            ind    = icnode[index]\n",
    "#            print('kriging node: index = ' + str(index) + ', x,y ' + str(x1) + ',' + str(y1) + ', value = ' + str(vra[j])) \n",
    "            ix1    = ix + (int(ixnode[ind])-nctx-1)\n",
    "            iy1    = iy + (int(iynode[ind])-ncty-1)\n",
    "#            print('ix1, iy1 = '); print(ix1,iy1)\n",
    "            index  = ix1 + (iy1-1)*nx\n",
    "            if lktype == 2: \n",
    "                vrea[j]= lvm[index]\n",
    "                vra[j] = vra[j] - vrea[j]\n",
    "        for i in range(0,na): # we need the full matrix \n",
    "#            print('kriging indice populated' + str(j) + ',' + str(i))\n",
    "# Sort out the actual location of point \"i\"\n",
    "            if i < nclose:\n",
    "                index  = int(close[i]) # adjust for 0 index\n",
    "                x2     = x[index]\n",
    "                y2     = y[index]\n",
    "            else:\n",
    "\n",
    "# It is a previously simulated node (keep index for table look-up):\n",
    "                #print('i = ' + str(i) + ',nclose = ' + str(nclose) + ', na = ' + str(na))\n",
    "                index  = i-(nclose)\n",
    "                x2     = cnodex[index]\n",
    "                y2     = cnodey[index]\n",
    "                ind    = icnode[index]\n",
    "#                print('previous node index' + str(ind))\n",
    "                ix2    = ix + (int(ixnode[ind])-nctx-1)\n",
    "                iy2    = iy + (int(iynode[ind])-ncty-1)\n",
    "\n",
    "# Now, get the covariance value:\n",
    "            iin = iin + 1\n",
    "#            print('kriging data location = '); print(x2,y2)\n",
    "# Decide whether or not to use the covariance look-up table:\n",
    "            if j <= nclose or i <= nclose:\n",
    "                cov = cova2(x1,y1,x2,y2,nst,c0,9999.9,cc,aa,it,ang,anis,rotmat,maxcov)\n",
    "                a[iin] = cov\n",
    "            else:\n",
    "\n",
    "# Try to use the covariance look-up (if the distance is in range):\n",
    "#                ii = nctx + 1 + (ix1 - ix2)\n",
    "#                jj = ncty + 1 + (iy1 - iy2)\n",
    "                cov = cova2(x1,y1,x2,y2,nst,c0,9999.9,cc,aa,it,ang,anis,rotmat,maxcov)\n",
    "#                if ii < 0 or ii >= MAXCTX or jj < 0 or jj >= MAXCTY:\n",
    "#                    cov = cova2(x1,y1,x2,y2,nst,c0,9999.9,cc,aa,it,ang,anis,rotmat,maxcov)\n",
    "#                else:\n",
    "#                    cov = covtab[ii,jj]\n",
    "#                print(x1,y1,x2,y2,cov)\n",
    "                a[iin] = cov\n",
    "\n",
    "# Get the RHS value (possibly with covariance look-up table):\n",
    "        if j <= nclose:\n",
    "#            print(cc,aa,it,ang,anis,rotmat,maxcov)\n",
    "            cov = cova2(xx,yy,x1,y1,nst,c0,9999.9,cc,aa,it,ang,anis,rotmat,maxcov)\n",
    "            r[j] = cov\n",
    "        else:\n",
    "\n",
    "# Try to use the covariance look-up (if the distance is in range):\n",
    "#            ii = nctx + 1 + (ix - ix1)\n",
    "#            jj = ncty + 1 + (iy - iy1)\n",
    "    \n",
    "#            print('RHS ctable coord' + str(ii) + ',' + str(jj))\n",
    "#            print('ix,iy ='); print(ix,iy)\n",
    "#            print('ix1,iy1'); print(ix1,iy1)\n",
    "#            if ii < 0 or ii >= MAXCTX or jj < 0 or jj >= MAXCTY: # adjusted for origin 0\n",
    "#                print('Not using covariance table')\n",
    "#                cov = cova2(xx,yy,x1,y1,nst,c0,9999.9,cc,aa,it,ang,anis,rotmat,maxcov)    \n",
    "#            else:\n",
    "#               cov = covtab[ii,jj]\n",
    "            cov = cova2(xx,yy,x1,y1,nst,c0,9999.9,cc,aa,it,ang,anis,rotmat,maxcov) \n",
    "            r[j] = cov\n",
    "#        print('kriging, writing RHS '+ str(j) + ',' + str(cov) + 'loc_est' + str(xx) + ',' + str(yy) + 'data' + str(x1) + ',' + str(y1))\n",
    "        rr[j] = r[j]\n",
    "\n",
    "\n",
    "# Addition of OK constraint:\n",
    "    if lktype == 1 or lktype == 3:\n",
    "        for i in range(0,j):\n",
    "            iin    = iin + 1\n",
    "            a[iin] = 1.0\n",
    "        iin       = iin + 1\n",
    "        a[iin]    = 0.0\n",
    "        r[na]  = 1.0\n",
    "        rr[na] = 1.0\n",
    "\n",
    "# Addition of the External Drift Constraint:\n",
    "    if lktype == 3:\n",
    "        edmin =  999999.\n",
    "        edmax = -999999.\n",
    "        for i in range(0,na):\n",
    "            iin    = iin + 1\n",
    "            a[iin] = vrea(i)\n",
    "            if a[iin] <edmin: edmin = a[iin]\n",
    "            if a[iin] > edmax: edmax = a[iin]\n",
    "        iin       = iin + 1\n",
    "        a[iin]   = 0.0\n",
    "        iin      = iin + 1\n",
    "        a[iin]    = 0.0\n",
    "        ind      = ix + (iy-1)*nx\n",
    "        r[na+1]  = lvm[ind]\n",
    "        rr[na+1] = r[na+1]\n",
    "        if (edmax-edmin) < EPSLON: neq = neq - 1\n",
    "\n",
    "# Addition of Collocated Cosimulation Constraint:\n",
    "    if lktype == 4:\n",
    "        sfmin =  1.0e21\n",
    "        sfmax = -1.0e21\n",
    "        for i in range(0,na):\n",
    "            iin    = iin + 1\n",
    "            a[iin] = colocorr*r[i]\n",
    "            if a[iin] < sfmin: sfmin = a[iin]\n",
    "            if a[iin] > sfmax: sfmax = a[iin]\n",
    "        iin    = iin + 1\n",
    "        a[iin] = 1.0\n",
    "        ii     = na + 1\n",
    "        r[ii]  = dble(colocorr)\n",
    "        rr[ii] = r[ii]\n",
    "        if (sfmax-sfmin) < EPSLON: neq = neq - 1\n",
    "\n",
    "# Solve the Kriging System:\n",
    "    if neq == 1 and lktype != 3:\n",
    "#        print('neq = 1 '); print(a,r)\n",
    "        s[0]  = r[0] / a[0]\n",
    "    else:\n",
    "#        print('neq = ' + str(neq)); print(a,r)\n",
    "        s = ksol_numpy(neq,a,r)\n",
    "        ising = 0 # need to figure this out\n",
    "\n",
    "# Compute the estimate and kriging variance.  Recall that kriging type\n",
    "#     0 = Simple Kriging:\n",
    "#     1 = Ordinary Kriging:\n",
    "#     2 = Locally Varying Mean:\n",
    "#     3 = External Drift:\n",
    "#     4 = Collocated Cosimulation:\n",
    "\n",
    "#    print('kriging weights'); print(s)\n",
    "    cmean  = 0.0\n",
    "#    print('cbb = ' + str(cbb))\n",
    "    cstdev = cbb \n",
    "    sumwts = 0.0\n",
    "    for i in range(0,na):\n",
    "        cmean  = cmean  + s[i]*vra[i]\n",
    "        cstdev = cstdev - s[i]*rr[i]\n",
    "        sumwts = sumwts + s[i]\n",
    "    if lktype == 1: cstdev = cstdev - s[na+1]\n",
    "    if lktype == 2: cmean  = cmean + gmean\n",
    "    if lktype == 4:\n",
    "        ind    = ix + (iy-1)*nx\n",
    "        cmean  = cmean  + s[na+1]*lvm[ind]\n",
    "        cstdev = cstdev - s[na+1] *rr[na+1]\n",
    "\n",
    "# Error message if negative variance:\n",
    "    if cstdev < 0.0:\n",
    "#        print('ERROR: Negative Variance: ' + str(cstdev))\n",
    "        cstdev = 0.0\n",
    "    cstdev = math.sqrt(max(cstdev,0.0))\n",
    "#    print('kriging estimate and variance' + str(cmean) + ', ' + str(cstdev))\n",
    "    return cmean, cstdev\n",
    "\n",
    "def getindex(nc,cmn,csiz,loc):\n",
    "    ic = min(int((loc - cmn) / csiz), nc - 1)\n",
    "    return ic\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math                        # for trig and constants\n",
    "import scipy.spatial as sp         # for fast nearest nearbour search\n",
    "from statsmodels.stats.weightstats import DescrStatsW\n",
    "#from numba import jit             # for precompile speed up \n",
    "# GSLIB's KB2D program (Deutsch and Journel, 1998) converted from the original Fortran to Python \n",
    "# translated by Michael Pyrcz, the University of Texas at Austin (Jan, 2019)\n",
    "\n",
    "def sgsim(df,xcol,ycol,vcol,wcol,scol,tmin,tmax,itrans,ismooth,dftrans,tcol,twtcol,zmin,zmax,ltail,ltpar,utail,utpar,nsim,\n",
    "          nx,xmn,xsiz,ny,ymn,ysiz,seed,ndmin,ndmax,nodmax,mults,nmult,noct,radius,radius1,sang1,\n",
    "          mxctx,mxcty,ktype,colocorr,sec_map,vario):\n",
    "    \n",
    "# Parameters from sgsim.inc\n",
    "    import random\n",
    "    random.seed(seed)\n",
    "    \n",
    "    MAXNST=2; MAXROT=2; UNEST=-99.0; EPSLON=1.0e-20; VERSION=2.907\n",
    "    KORDEI=12; MAXOP1=KORDEI+1; MAXINT=2**30\n",
    "    \n",
    "    nxy = nx*ny\n",
    "    sstrat = 0 # search data and nodes by default, turned off if unconditional\n",
    "    radsqd = radius * radius\n",
    "    sanis1 = radius1/radius\n",
    "    if ktype == 4: varred = 1.0\n",
    "        \n",
    "# load the variogram\n",
    "    nst = int(vario['nst'])\n",
    "    cc = np.zeros(nst); aa = np.zeros(nst); it = np.zeros(nst,dtype=int)\n",
    "    ang = np.zeros(nst); anis = np.zeros(nst)\n",
    "    \n",
    "    c0 = vario['nug']; \n",
    "    cc[0] = vario['cc1']; it[0] = vario['it1']; ang[0] = vario['azi1']; \n",
    "    aa[0] = vario['hmaj1']; anis[0] = vario['hmin1']/vario['hmaj1'];\n",
    "    if nst == 2:\n",
    "        cc[1] = vario['cc2']; it[1] = vario['it2']; ang[1] = vario['azi2']; \n",
    "        aa[1] = vario['hmaj2']; anis[1] = vario['hmin2']/vario['hmaj2'];        \n",
    "\n",
    "# Set the constants\n",
    "    MAXCTX = mxctx\n",
    "    MAXCTY = mxcty\n",
    "    MAXCXY = MAXCTX * MAXCTY\n",
    "    MAXX   = nx\n",
    "    MAXY   = ny\n",
    "    MAXZ   = 1 # assuming 2D for now\n",
    "    MXY  = MAXX * MAXY\n",
    "    if MXY < 100: MXY = 100\n",
    "    MAXNOD = nodmax\n",
    "    MAXSAM = ndmax\n",
    "    MAXKR1 = MAXNOD + MAXSAM + 1\n",
    "    MAXKR2 = MAXKR1 * MAXKR1\n",
    "    MAXSBX = 1\n",
    "    if nx >  1:\n",
    "        MAXSBX = int(nx/2)\n",
    "        if MAXSBX > 50: MAXSBX=50\n",
    "    MAXSBY = 1\n",
    "    if ny > 1: \n",
    "        MAXSBY = int(ny/2)\n",
    "        if MAXSBY > 50: MAXSBY=50\n",
    "\n",
    "    MAXSBZ = 1\n",
    "    MAXSB = MAXSBX*MAXSBY*MAXSBZ\n",
    "    \n",
    "# Declare arrays\n",
    "\n",
    "    dist = np.zeros(ndmax)\n",
    "    nums = np.zeros(ndmax,dtype = int)\n",
    "    \n",
    "# Perform some quick checks\n",
    "    if nx > MAXX or ny> MAXY:\n",
    "        print('ERROR: available grid size: ' + str(MAXX) + ',' + str(MAXY) + ',' + str(MAXZ) +'.')\n",
    "        print('       you have asked for : ' + str(nx) + ',' + str(ny) + ',' + str(nz) + '.')\n",
    "        return sim\n",
    "    if ltail != 1 and ltail != 2:\n",
    "        print('ERROR invalid lower tail option ' + str(ltail))\n",
    "        print('      only allow 1 or 2 - see GSLIB manual ')\n",
    "        return sim\n",
    "    if utail != 1 and utail != 2 and utail != 4:\n",
    "        print('ERROR invalid upper tail option ' + str(ltail))\n",
    "        print('      only allow 1,2 or 4 - see GSLIB manual ')\n",
    "        return sim \n",
    "    if utail == 4 and utpar < 1.0:\n",
    "        print('ERROR invalid power for hyperbolic tail' + str(utpar))\n",
    "        print('      must be greater than 1.0!')\n",
    "        return sim\n",
    "    if ltail == 2 and ltpar < 0.0:\n",
    "        print('ERROR invalid power for power model' + str(ltpar))\n",
    "        print('      must be greater than 0.0!')\n",
    "        return sim\n",
    "    if utail == 2 and utpar < 0.0: \n",
    "        print('ERROR invalid power for power model' + str(utpar))\n",
    "        print('      must be greater than 0.0!')\n",
    "        return sim\n",
    "     \n",
    "# Load the data\n",
    "    df_extract = df.loc[(df[vcol] >= tmin) & (df[vcol] <= tmax)]    # trim values outside tmin and tmax\n",
    "    nd = len(df_extract)\n",
    "    ndmax = min(ndmax,nd)\n",
    "    x = df_extract[xcol].values\n",
    "    y = df_extract[ycol].values\n",
    "    vr = df_extract[vcol].values\n",
    "    wt = []; wt = np.array(wt)\n",
    "    if wcol > -1: \n",
    "        wt = df_extract[wcol].values \n",
    "    else:\n",
    "        wt = np.ones(nd)\n",
    "    sec = []; sec = np.array(sec)\n",
    "    if scol > -1:\n",
    "        sec = df_extract[scol].values\n",
    "    if itrans == 1:\n",
    "        if ismooth == 1:\n",
    "            dftrans_extract = dftrans.loc[(dftrans[tcol] >= tmin) & (dftrans[tcol] <= tmax)]  \n",
    "            ntr = len(dftrans_extract)\n",
    "            vrtr = dftrans_extrac[tcol].values\n",
    "            if twtcol > -1: \n",
    "                vrgtr = dftrans_extrac[tcol].values\n",
    "            else:\n",
    "                vrgtr = np.ones(ntr) \n",
    "        else:\n",
    "            vrtr = df_extract[vcol]\n",
    "            ntr = len(df)  \n",
    "            vrgtr = np.copy(wt)\n",
    "        twt = np.sum(vrgtr)\n",
    "# sort        \n",
    "        vrtr,vrgtr = dsortem(0,ntr,vrtr,2,b=vrgtr)\n",
    "\n",
    "# Compute the cumulative probabilities and write transformation table\n",
    "        twt   = max(twt,EPSLON)\n",
    "        oldcp = 0.0\n",
    "        cp    = 0.0\n",
    "        for j in range(0,ntr):               \n",
    "            cp =  cp + vrgtr[j]/twt\n",
    "            w  = (cp + oldcp)*0.5\n",
    "            vrg = gauinv(w)\n",
    "            trans_table[j,0] = vrtr[j]\n",
    "            trans_table[j,1] = vrg\n",
    "            oldcp =  cp\n",
    "# Now, reset the weight to the normal scores value:\n",
    "            vrgtr[j] = vrg\n",
    "              \n",
    "        twt = np.sum(wt)             \n",
    "# Normal scores transform the data\n",
    "        for id in range(0,nd):\n",
    "            if itrans == 1: \n",
    "                vrr = vr[id]\n",
    "                j = locate(vrtr,1,ntr,vrr)\n",
    "                j   = min(max(0,j),(ntr-2))\n",
    "                vrg = powint(vrtr[j],vrtr[j+1],vrgtr[j],vrgtr[j+1],vrr,1.0)\n",
    "                if vrg < vrgtr(0): vrg = vrgtr(0)\n",
    "                if(vrg > vrgtr[ntr-1]): vrg = vrgtr[nd-1]\n",
    "                vr[id] = vrg   \n",
    "                \n",
    "    weighted_stats = DescrStatsW(vr,weights=wt)\n",
    "    av = weighted_stats.mean        \n",
    "    ss = weighted_stats.var        \n",
    "\n",
    "    print('\\n Data for SGSIM: Number of acceptable data  = ' + str(nd))\n",
    "    print('                 Number trimmed               = ' + str(len(df)- nd))\n",
    "    print('                 Weighted Average             = ' + str(av))\n",
    "    print('                 Weighted Variance            = ' + str(ss))\n",
    "              \n",
    "# Read in secondary data           \n",
    "    lvm = []; lvm = np.array(lvm)\n",
    "    if ktype >= 2:\n",
    "        lvm = np.copy(sec_map.flatten())\n",
    "        for ireal in range(0,nreal): \n",
    "            index = -1\n",
    "            for ixy in range(0,nxy): \n",
    "                index = index + 1\n",
    "                sim[index] = index \n",
    "        if ktype == 2 and itrans == 1: \n",
    "            for ixy in range(0,nxy): \n",
    "# Do we to transform the secondary variable for a local mean?\n",
    "                vrr = lvm[ixy]\n",
    "                j = locate(vrtr,1,ntr,vrr)\n",
    "                j   = min(max(0,j),(ntr-2))\n",
    "                vrg = powint(vrtr(j),vrtr(j+1),vrgtr(j),vrgtr(j+1),vrr,1.0)\n",
    "                if vrg < vrgtr(0): vrg = vrgtr(0)\n",
    "                if(vrg > vrgtr(ntr-1)): vrg = vrgtr(nd-1)\n",
    "                lvm[ixy] = vrg \n",
    "        av = np.average(lvm)\n",
    "        ss = np.var(lvm)\n",
    "        print(' Secondary Data: Number of data             = ' + str(nx*ny))\n",
    "        print('                 Equal Weighted Average     = ' + str(avg))\n",
    "        print('                 Equal Weighted Variance    = ' + str(ss))             \n",
    "\n",
    "# Do we need to work with data residuals? (Locally Varying Mean)\n",
    "        if ktype == 2:\n",
    "            for idd in range(0,nd): \n",
    "                ix = getindex(nx,xmn,xsiz,x[idd])\n",
    "                iy = getindex(ny,ymn,ysiz,y[idd])\n",
    "                index = ix + (iy-1)*nx\n",
    "                sec[index] = lvm[index]\n",
    "# Calculation of residual moved to krige subroutine: vr(i)=vr(i)-sec(i)\n",
    "\n",
    "\n",
    "# Do we need to get an external drift attribute for the data?\n",
    "        if ktype == 3:\n",
    "            for idd in range(0,nd): \n",
    "                if sec[i] != UNEST:\n",
    "                    ix = getindx(nx,xmn,xsiz,x[idd])\n",
    "                    iy = getindx(ny,ymn,ysiz,y[idd])\n",
    "                    ind = ix + (iy)*nx\n",
    "                    sec[ind] = lvm[ind]\n",
    "\n",
    "# Transform the secondary attribute to normal scores?\n",
    "        if ktype == 4:\n",
    "            print(' Transforming Secondary Data with')\n",
    "            print(' variance reduction of ' + str(varred))\n",
    "            for ireal in range(0,nreal):             \n",
    "                lvm,sim = dsortem(0,nxy,lvm,1,b=sim)\n",
    "                oldcp = 0.0\n",
    "                cp    = 0.0\n",
    "                for i in range(0,nxy): \n",
    "                    cp =  cp + dble(1.0/real(nxyz))\n",
    "                    w  = (cp + oldcp)/2.0\n",
    "                    lvm[i] = gauinv(w)\n",
    "                    lvm[i] = lvm[i] * varred\n",
    "                    oldcp  =  cp\n",
    "                sim,lvm = dsortem(0,nxy,sim,1,b=lvm) \n",
    "\n",
    "# Set up the rotation/anisotropy matrices that are needed for the\n",
    "# variogram and search.\n",
    "    print('Setting up rotation matrices for variogram and search') \n",
    "    if nst == 1:\n",
    "        rotmat = setrot(ang[0],ang[0],sang1,anis[0],anis[0],sanis1,nst,MAXROT=2)\n",
    "    else:\n",
    "        rotmat = setrot(ang[0],ang[1],sang1,anis[0],anis[1],sanis1,nst,MAXROT=2)\n",
    "    isrot = 2 # search rotation is appended as 3rd\n",
    "\n",
    "    rotmat_2d, maxcov = setup_rotmat(c0,nst,it,cc,ang) # will use one in the future\n",
    "    print('MaxCov = ' + str(maxcov))\n",
    "    \n",
    "# Make a KDTree for fast search of nearest neighbours   \n",
    "    dp = list((y[i], x[i]) for i in range(0,nd))\n",
    "    data_locs = np.column_stack((y,x))\n",
    "    tree = sp.cKDTree(data_locs, leafsize=16, compact_nodes=True, copy_data=False, balanced_tree=True)\n",
    "              \n",
    "# Set up the covariance table and the spiral search:\n",
    "    cov_table,tmp,order,ixnode,iynode,nlooku,nctx,ncty = ctable(MAXNOD,MAXCXY,MAXCTX,MAXCTY,MXY,\n",
    "                                isrot,nx,ny,nst,c0,cc,aa,it,ang,anis,rotmat,radsqd)\n",
    "       \n",
    "#    print('Covariance Table'); print(cov_table)\n",
    "# MAIN LOOP OVER ALL THE SIMULAUTIONS:\n",
    "    for isim in range(0,nsim):\n",
    "          \n",
    "# Work out a random path for this realization:\n",
    "        sim = np.random.rand(nx*ny)\n",
    "        order = np.zeros(nxy)\n",
    "        ind = 0\n",
    "        for ixy in range(0,nxy): \n",
    "            order[ixy] = ind  \n",
    "            ind = ind + 1\n",
    "            \n",
    "# The multiple grid search works with multiples of 4 (yes, that is\n",
    "# somewhat arbitrary):\n",
    "\n",
    "        if mults == 1:\n",
    "            for imult in range(0,nmult): \n",
    "                nny = int(max(1,ny/((imult+1)*4)))\n",
    "                nnx = int(max(1,nx/((imult+1)*4)))\n",
    "#                print('multi grid - nnx, nny'); print(nnx,nny)\n",
    "                jy  = 1\n",
    "                jx  = 1\n",
    "                for iy in range(0,nny): \n",
    "                    if nny > 0: jy = iy*(imult+1)*4\n",
    "                    for ix in range(0,nnx):\n",
    "                        if nnx > 0: jx = ix*(imult+1)*4\n",
    "                        index = jx + (jy-1)*nx\n",
    "                        sim[index] = sim[index] - (imult+1)\n",
    "\n",
    "    \n",
    "# Initialize the simulation:\n",
    "        sim, order = dsortem(0,nxy,sim,2,b=order)\n",
    "        sim.fill(UNEST)\n",
    "        print('Working on realization number ' + str(isim))\n",
    "\n",
    "# Assign the data to the closest grid node:\n",
    "\n",
    "        TINY = 0.0001\n",
    "        for idd in range(0,nd):\n",
    "#            print('data'); print(x[idd],y[idd])\n",
    "            ix = getindex(nx,xmn,xsiz,x[idd])\n",
    "            iy = getindex(ny,ymn,ysiz,y[idd])\n",
    "            ind = ix + (iy-1)*nx \n",
    "            xx  = xmn + (ix)*xsiz\n",
    "            yy  = ymn + (iy)*ysiz\n",
    "#            print('xx, yy' + str(xx) + ',' + str(yy))\n",
    "            test = abs(xx-x[idd]) + abs(yy-y[idd])\n",
    "\n",
    "# Assign this data to the node (unless there is a closer data):\n",
    "            if sstrat == 1:\n",
    "                if sim[ind] > 0.0:\n",
    "                    id2 = int(sim[ind]+0.5)\n",
    "                    test2 = abs(xx-x(id2)) + abs(yy-y(id2))\n",
    "                    if test <= test2: \n",
    "                        sim[ind] = idd\n",
    "                    else:\n",
    "                        sim[ind] = id2\n",
    "\n",
    "# Assign a flag so that this node does not get simulated:\n",
    "            if sstrat == 0 and test <= TINY: sim[ind]=10.0*UNEST\n",
    "\n",
    "# Now, enter data values into the simulated grid:\n",
    "        for ind in range(0,nxy):              \n",
    "            idd = int(sim[ind]+0.5)\n",
    "            if idd > 0: sim[ind] = vr[id]\n",
    "        irepo = max(1,min((nxy/10),10000))          \n",
    "\n",
    "# MAIN LOOP OVER ALL THE NODES:\n",
    "        for ind in range(0,nxy):  \n",
    "            if (int(ind/irepo)*irepo) == ind:\n",
    "                print('   currently on node ' + str(ind))\n",
    "              \n",
    "# Figure out the location of this point and make sure it has\n",
    "# not been assigned a value already:\n",
    "\n",
    "            index = int(order[ind]+0.5)\n",
    "            if (sim[index] > (UNEST+EPSLON)) or (sim[index] < (UNEST*2.0)): continue\n",
    "            iy   = int((index)/nx) \n",
    "            ix   = index - (iy)*nx\n",
    "            xx = xmn + (ix)*xsiz\n",
    "            yy = ymn + (iy)*ysiz   \n",
    "            current_node = (yy,xx)\n",
    "#            print('Current_node'); print(current_node)\n",
    "\n",
    "# Now, we'll simulate the point ix,iy,iz.  First, get the close data\n",
    "# and make sure that there are enough to actually simulate a value,\n",
    "# we'll only keep the closest \"ndmax\" data, and look for previously\n",
    "# simulated grid nodes:\n",
    "\n",
    "            if sstrat == 0:\n",
    "        \n",
    "                na = -1   # accounting for 0 as first index\n",
    "                if ndmax == 1:\n",
    "                    dist = np.zeros(1); nums = np.zeros(1)\n",
    "                    dist[0], nums[0] = tree.query(current_node,ndmax) # use kd tree for fast nearest data search\n",
    "                else:\n",
    "                    dist, nums = tree.query(current_node,ndmax)\n",
    "                # remove any data outside search radius\n",
    "                \n",
    "#                print('nums'); print(nums)\n",
    "                na = len(dist)\n",
    "                nums = nums[dist<radius]\n",
    "                dist = dist[dist<radius] \n",
    "                na = len(dist) \n",
    "                if na > 0:\n",
    "#                    print(MAXROT)\n",
    "                    for idd in range(0,na):  # remove any data outside anisotropic search limits                \n",
    "#                        print('distance check'); print(xx,x[int(nums[idd])],yy,y[int(nums[idd])])\n",
    "                        dist[idd] = sqdist(xx,yy,0.0,x[int(nums[idd])],y[int(nums[idd])],0.0,MAXROT,rotmat)\n",
    "#                        print(dist[idd]); \n",
    "                    nums = nums[dist<1.0]\n",
    "                    dist = dist[dist<1.0] \n",
    "                    na = len(dist)\n",
    "                if na < ndmin: continue     # bail if not enough data\n",
    "                    \n",
    "                    \n",
    "#            print('node search inputs')\n",
    "#            print('nodmax ' + str(nodmax))\n",
    "#            print('ixnode'); print(ixnode)\n",
    "                    \n",
    "            ncnode, icnode, cnodev, cnodex, cnodey = srchnd(ix,iy,sim,noct,nodmax,ixnode,iynode,nlooku,nctx,ncty,UNEST)\n",
    " #           print('srchnd'); print(ncnode,icnode,cnodev,cnodex,cnodey)\n",
    " #           print('Result of srchnd, cnodex = '); print(cnodex)\n",
    "            nclose = na\n",
    " #           print('srch node, nclose ' + str(nclose) + ', ncnode ' + str(ncnode))\n",
    " #           print('nums'); print(nums)\n",
    "# Calculate the conditional mean and standard deviation.  This will be\n",
    "# done with kriging if there are data, otherwise, the global mean and\n",
    "# standard deviation will be used:\n",
    "\n",
    "            if ktype == 2:\n",
    "                gmean = lvm[index]\n",
    "            else:\n",
    "                gmean = 0.0\n",
    "\n",
    "            if nclose+ncnode < 1:\n",
    "                cmean  = gmean\n",
    "                cstdev = 1.0\n",
    "\n",
    "#Perform the kriging.  Note that if there are fewer than four data\n",
    "# then simple kriging is prefered so that the variance of the\n",
    "# realization does not become artificially inflated:\n",
    "\n",
    "            else:\n",
    "                lktype = ktype\n",
    "                if ktype == 1 and (nclose+ncnode) < 4: lktype=0\n",
    "                cmean, cstdev = krige(ix,iy,xx,yy,lktype,x,y,vr,sec,lvm,nums,cov_table,nctx,ncty,icnode,ixnode,iynode,cnodev,cnodex,cnodey,\n",
    "                                      nst,c0,9999.9,cc,aa,it,ang,anis,rotmat_2d,maxcov,MAXCTX,MAXCTY,MAXKR1,\n",
    "                                      MAXKR2)\n",
    "\n",
    "# Draw a random number and assign a value to this node:\n",
    "            p = random.uniform(0, 1)\n",
    "            xp = gauinv(p)\n",
    "            sim[index] = xp * cstdev + cmean\n",
    "#            print('simulated value = ' + str(sim[index]))\n",
    "\n",
    "# Quick check for far out results:\n",
    "            if abs(cmean) > 5.0 or abs(cstdev) > 5.0 or abs(sim[index]) > 6.0:\n",
    "                print('WARNING: grid node location: ' + str(ix) + ',' + str(iy))\n",
    "                print('         conditional mean and stdev:  ' + str(cmean) + ',' + str(cstdev))\n",
    "                print('         simulated value:    ' + str(sim[index]))\n",
    "\n",
    "# Do we need to reassign the data to the grid nodes?\n",
    "        if sstrat == 0:\n",
    "            for iid in range(0,nd):  \n",
    "                ix = getindex(nx,xmn,xsiz,x[idd])\n",
    "                iy = getindex(ny,ymn,ysiz,y[idd])\n",
    "                xx  = xmn + (ix)*xsiz\n",
    "                yy  = ymn + (iy)*ysiz\n",
    "                ind = ix + (iy-1)*nx \n",
    "                test=abs(xx-x[iid])+abs(yy-y[iid])\n",
    "                if test <= TINY: sim[ind] = vr[id]\n",
    "\n",
    "# Back transform each value and write results:\n",
    "        ne = 0\n",
    "        av = 0.0\n",
    "        ss = 0.0\n",
    "        for ind in range(0,nxy):\n",
    "            iy = int((index-1)/nx) + 1\n",
    "            ix = index - (iy-1)*nx\n",
    "            simval = sim[ind]\n",
    "            if simval > -9.0 and simval < 9.0:\n",
    "                ne = ne + 1\n",
    "                av = av + simval\n",
    "                ss = ss + simval*simval\n",
    "            if itrans == 1 and simval > (UNEST+EPSLON):\n",
    "                simval = backtr_value(simval,vrtr,vrgtr,zmin,zmax,ltail,ltpar,utail,utpar)\n",
    "                if simval < zmin: simval = zmin\n",
    "                if simval > zmax: simval = zmax\n",
    "            sim[ind] = simval\n",
    "        av = av / max(ne,1.0)\n",
    "        ss =(ss / max(ne,1.0)) - av * av\n",
    "        print('\\n Realization ' + str(isim) + ': number   = ' + str(ne))\n",
    "        print('                                   mean     = ' + str(round(av,4)) + ' (close to 0.0?)')\n",
    "        print('                                   variance = ' + str(round(ss,4)) + ' (close to gammabar(V,V)? approx. 1.0)')\n",
    "\n",
    "# END MAIN LOOP OVER SIMULATIONS:\n",
    "        sim_out = np.zeros((ny,nx))\n",
    "        for ind in range(0,nxy):\n",
    "            iy   = int((ind)/nx) \n",
    "            ix   = ind - (iy)*nx\n",
    "            sim_out[ny-iy-1,ix] = sim[ind]\n",
    "    return sim_out\n",
    "\n",
    "    #return np.reshape(order,(100,100)) # checked the random path - passed\n",
    "\n",
    "        \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Data for SGSIM: Number of acceptable data  = 1\n",
      "                 Number trimmed               = 0\n",
      "                 Weighted Average             = [-0.5]\n",
      "                 Weighted Variance            = [0.]\n",
      "Setting up rotation matrices for variogram and search\n",
      "MaxCov = 1.0\n",
      "Working on realization number 0\n",
      "   currently on node 0\n",
      "   currently on node 1000\n",
      "   currently on node 2000\n",
      "   currently on node 3000\n",
      "   currently on node 4000\n",
      "   currently on node 5000\n",
      "   currently on node 6000\n",
      "   currently on node 7000\n",
      "   currently on node 8000\n",
      "   currently on node 9000\n",
      "\n",
      " Realization 0: number   = 10000\n",
      "                                   mean     = -0.0609 (close to 0.0?)\n",
      "                                   variance = 0.9464 (close to gammabar(V,V)? approx. 1.0)\n"
     ]
    },
    {
     "data": {
      "image/png": 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zJQ1g+1Qbie48Z7dHaiPavvPQxo5LY04P+Mb1U6rs8yjREKqaIKyRgcY0gkYr6jpANwpj7DPb990gO+9SKg0CjJY0WtJoRdPMxlAYVqioRoQ1GEFThNRVQNO+W4kxAiEMUVi141wFNUIa6jKkKCMKrRACJHbMKTenpH+Xbm4JYZCqQQQ2tU+X9n5AZ/wZTCPRWqG1tH0gG4Rs0FpR1iGltv1tgLoRJKohCWriqOyMM4FQDVJqZNCAaBACkLN3bT1Hs96v84ggKRBhg9ncRNQVHOeUk5SsjPAstgLTzsVQ1kRRRRBXiEBjKkVdhlRVSBhVhL0cEUtMqSlHPcoqom4kyrVH0Nh+EQYpjWtzMzdfjBFEr50RI33gAx/YN8a0RBb3gy//is8x+/uj+7rGz3/g2R83xnzSWvE+0QLxthDiqjHmpjOJ3nHHrzPP2nINy0ZxHVsNoXv8vcsubCxB7w8BfNpg2/yNz3wrWRWxn6cclRF7hX3UpwcZT28csdUf0zSS2ydb7OcpADtJxuX+iF6Ss/v4HhvXbpO8+gDRC6GfQqCof6nihZ97I7/w3NPs5SmBMAzCikTVBEqTBBV5HfLcySbPT2OeHQnGuqFuGgaBYisSDDsEY1UDw9BwNS15zWDEVjqhqEMO8x6h1GwmGduDEcPBhLIMGU36nGQ9xmVMrgOyevYKAzd5cq3ItRV2W1HJVlSQhiW9qGCjPyFNc8oy5PBkg4PJ0H5X2YVpM5myvXHCYGMMwM2bj3PjeJvnJ4N242CfV3NteMwwndI0pw0NUVjRSzOiuCSMSxqtGJ9YBjCtFXkZtfcNlEa5+yulieISFcznQOtakU1TRpM+Ujb004zBxhhdK+7s7cy9xzSoebxvJ/arrtzi8qtuIENLtxo/foj8nB2mn/fbSLffTJ59jOgj/zfBhz6IfmEmqEwZMH1hl8Obj3FwuMWkSBgXCbkO2r7NtWIrKhlEBaHUZFXEURmznyckStsN1oKgT4N67v9RFTKuQnItSVTDIKwIhKE2diM3CCtC2aCEYRAVXN26y+7je/QvH1HnEeO7W+wfXOJoMqBqFEUdMIgKNtIpWxsn9DfGxP0p8bbtj+qkz+Rwk4P9S0zzBO3e3fZwxM6VPXo7h9TThMObj1GWIVorpnnCpEgAeHL3Do89fR0Z1uz92qva5/j4rSs8c3SJXCsSpUmUZhiW7fjtpad515VqSPqWZXDv9i7TPGGjP2EwHNPbGCODmmLS4+Rwk9GkT1GFKNkQhRWjrMf10SZHZcS4CqjcJuSpXsbrt/d56ombbtwEaC1J+xnpxoh4e4RKSmRYI6K6fddNFWD0/DgWyo6n8AsHiJMTxu/b5fqHX8szt6+2m5pAaYo6oGoUVwbHPPXETS6/7nmiyycUt7e5+7EnuHHLstC99nXPsfGaG0xvXuZXf/nTmJYxVaMIpR3rcVARh1U7d9J+Rtyz/dZoRVVEHB9u8sYf+/ezNgrRpbW7L+zvT3n/f/6B+7pGqL7qohSCL0t8ogXiv8RWKfge9/tfdI5/gxDiXdigmmMnNH8c+C4xqyrxZXQYXFbBGNEuTl4YajOvmXmkYUlQWmIUvzhvbR2TboyJHjuC6GyWr9oIcm0Hte/MWit3fCYMz4I2MK4CbmU9cj17JUoY4sAJlqRABTVaK2qtqDraXeUWtbAjEBfhheFgOCbpZ0RFZHfejeQo6586fxn8Ig9QN5LbTpj2Y8tMJWVDElmtMYoqVFATdgRi2svIpmkrDBcRRRVJf0rcnyJVYzWZOqAqInQ9e6amkUwyu1hprSiq8JSg2oxzLvdHbD623wpD26mn34UJIuglyMEJRgtMKRFRjQxroqRAOi0nUBrc+zkqI7LaLf7u/Ws3Fo5KRT8QbMclgWxageahzekxlajGCtewIpR67v16hK4Ni5uFLpTTyqLQWhsWz220pMxjyiqcW5BPJn3UnkbXduEty1O0sOxuHrG1e5f02h6ohs1Rj+M7O9y4s8szR5cAa5EIhGmfue23Dsoymo2B4w17rDp9v2XQjWSU9difDsi1ajdpAKEwpEGNkmfPt7MgVDM/XgCev4U+luTH11aO3Yu0++PPPcXVIuJg/1K7EfFQsjmz3bpWZJOUk8nF5uqLg8aST71y8dAEohDiR7Da3Y6wFbz/PFYQvlsI8Uew5Vk8rdm/wUaYPoMlE/7DYMmAXRjzf3HnfYdj4D8TBsGkDjkqI0ZuIVUCEmcOW1xsrvZHKNkwTKf00wwVaBotMVlEQ4lMNcJxMVejbbIsmRNc1iSriKnnjo0qyLUmcPaV2hhyLQilbY9eMP7WjXTCdX5i6CUCrisU/eKz0xu7Nm5SN5LaCELZkIYlUVhRa2V3zLVC11aw1gt9ob2JyvWbv0+iTi/CVSM5zlN0I9lIpyRRSRStJjyxu3XVLn6B0qAWF2xFUwdATVMH9n/X7sItRlI2dmdeRtRaUdQh2ghqI8hqRd5IksmAy/2Ru9YMJougrpDZAc1mgdEZoi6hPv186bU9ZFgjleaF609Q1LbduVaMq8BueKqQYVhSNYpcBxwWEXdLiZL2fSZn9Ee1RLNehVBqtvpjNrePrfkTkE7g9dOMrIihtu/LCyGvfQGYWiECTVMHlGXIpEjIqsj2m1Ao2bSbjFXIy4hGK0xp+7QuYg4Ot3hhZAt2JEozTDLioCYJKvpxztbGrACJ1oqijCgre3/t5k0odWta9WO0cWO+0ardBI7LhKIOyHVA1cg5YdiFbiTavXfttD5d23Fl6tNz6TxUzw8wWlIVUWsN0c78XXfm5rhMKIsIncXUJz3KUY9smjItYyvsKnj+uiUOqtu5a589DqpTG4e5NhQRJ+MB0zJeec59wxg6hWtekXhoAtERMi/Dly4ecJUf/sSK67wDeMeDaFPodum1W5T9Lq1rSvSLSHYyhI9DkMwvaHeevcaHblquYm/auihy3bgfxW4y/z0lDInSp4ThIrwAOHITw37HmlZ3N49a0+Nzx9vkWrVapv/uaNxHa0lZRoymPXK3yPuJPS4T6mPV/n8wGaKNIFF6qeYJtBrJWcJwsf0A0zJmS1oh7ttd5jFlHqOcabEsIqZZyjS3JrsoPHvhaO9jBEUVUhURURW0u/5q1EP80jFJ8NPURzdIRwfI557FHC9ve/z4ISotqIqI4sbV1rx1HkL3PhNl79vdQM218xyh6BfezSRje/MEqTSyY3aVztycxoV9vkZZs1tkNXMZ1NRFTFMHyKBmejJgkqWnNJSLYJT12L+1S+DutX9rl4PJkKqRDMOKnXRCLyqIwmrl5qh2ws2PO99mu7kLqRv7udYSpRqyLOFwNGyF7nnI6oBxmTAa99v7NY1EyoaNIqJfRAyv7BMMMiS0ZtN7QdfM6f/XRrCf9aivv4qyjEjTnMPjDfZGloHuLO0vDip6Sd62taxC4qics4p8YmBoLkzX+mji5RJU81ARCoOStGYrbQRFHRIHFTsbx629PoxLImezz04GZCdD9F27Y/X+lGduX21NXoFs4EUsLONaU2eSnVi22mIoDYFsSFTdTrBlprWyCjnOZzv5UGou98bsbB2xvXsXqbT10dUh18dDElUTuwXKL0Yn4wFlFVLUoV183KILdgEZO1Oqkg2TzsLl/WHdTYAXuHFUEsV2AdT18mGltZ3seee+ADsbxyilrUnYfTebphRlxDRPmJYxvcguwhcRhmAFTd1Y81+dRwTQCkU9SRC/eovw5ARzPMWUS+K0VINMNUSC6NIJWyd3yKYp4zKxgUpiVczXrF8GYcUgztGNnPM1AWdqOB7jKqQ2gl3nh7Nm81nAE4AKrM81KctWe5GyIYpLgtj6yrymXRUR2TRd6vO9CHQj2TveIrpp3/Pe8RZFHZAozU5v3PoKvd9YBdYE22hFmcdot+DfGW9QNYqBe6ce3bHnhZj3FV6f9LjWn565YaycyXpcxu0GqqjteAulpqxCSmdl2Hr6BVtQ7UWiahQs2RwdlTEfunmN7WRKVs385KsQB9ZnGEclMTDJUhc8NtOQ5QXH/P3CsNYQXxECcRGVi+BchFR6zn+QTVKOTjbmAiq6Qirs+NOWIZSQKDuwA/e1w7rihIyoCoAeV1NJokwbhBAHNdTAEh+SrgMmRcJens4FdAx7U6Koas9TKyaQ95HmZUSgNL2oIA4qijpkvGCKOcx7pwRyIJu5iNyuOVY539ZZu1pv+iqc0JtpBpD2MyvM64AsS9pAjuM85bBI2oCn85AGmhTN1d6EYTq1ptaJraoUYAWI0ZJ6f4DKclDSCr4FiMi+W+FenAzruX4NpWErWt7Pw9CQBrrVIpRsTpnpvUm77m6uOrC+SLug7ianA1KWQS5cw49lGdRURUQ+6bVm5heDqrGm1dsHl20b6/DUc3m/pRXc9vm9+VNrxcFkyM1p3wp6I9h0/udaq6Va9M3JkGfHfZ6fSHLd51o/ZxjasR7IhsSdl7j/t+MCbQTHuX3nNvjMuiE2/VhLc7I72/SuHqDuQUOUSiNl0/p4l/l560ayX4Xs5wmDsGIrKkiCisCZRs9DoDTTKiQvI9IVG8uHBUGAYFWRlFcGXpECcRmySdruZgEmJwNOxgOryXT8Bl2/j1/U/Ge1VmgXXAFW8wsErQ8RIOykfgZCkChIpI0u9L6XUOpTAmoRgTPJ9aKCOCpRQd0uQF7wdAV1dzHvu4g/f17sfHr+nssm+uK9Q9nQDyp6UeGiRE9P9rKIKIuovdfhyQZ3xhvt54mqbVRvarVzqTSVO9+b1arGBihpI05tYmqtePbgMfbzZCZYhGE7LnhscHKm/w5ofWE6Y2XEYVMF6Czm5jOv5pmbT7aBWrb9tk9HThPwEb+JMqeiS7vwYyjXisxtILx/+/S59126sQ1M8hsNLyw8ch2QTweMi4RBlhN0BLlupDWja0WgtNV2zzAbay3RtSLsDF9dWxPoyaTPcTF7V4s+VL+BCNRp4aGN1QCtv17ObcwGLpq3G8Hrhb52fnQlDElQsdGfkPSnp1wh9wIlDCwJfAo7G0b/f6B067K4CHy7R1mPUdZjeLxBoDSH4yHjIllqNXpQMNQ05uChXf+TAWuB2EFZRK3JbpqdbVryEW5+cidKO4E5myShhETOXyMQgqgJUEgSJRgGDcOwtv6moCIOqjbY4KydfCAbBlFBL8ldROdCmoLTProTaDGlwQbXBG1EoQ/bPw/etDuI8wv59Moy4nA05GBq0y6UMG0aSC/JSfsZQVwgVUMYl7bIFsyZVZfh2YPHuNUJBBkENdtxzuXemF6St2bYi0BPEnQeYWpF4/Lh6jyiLmL2b+3yzO2rHBYJY9c/3fQI61uN2s9CYc3f6gyh2BWGocuHW9QSHxR8UNLJpH9KGM63yQpG2yZ9ZvsXoRs5N151rZzG7+49HnCc98hqmy+67Fn9/UKpW6HrrTBVY4WiT0epjbARrc5Skah6ziWyOG4GUUE/zknT3G6+FiNJLwhvZUGrOaGohDl1Ty+E/fy4V81cN5KjyYCxiwZ++DCYZm0yfeQgMLMgFRGAFCTSCp5hWJ3yXXTho9JWhYG3pq7Ob8DlXynUYugoEMju3xIaCIWy/kPpzY96LkQ9dBF3y+BNaKHUbQLyXBtdZKiPfoWZMOz6dmQnhF9mDUlQrRSIoWzmdvQ+pN5fexVOxgNGWa/1e3ph6H0n3e92FykfeOPf4yI+cucqHzreYFILLkea7bgkDWriwPpMz2tXF9WoZyMD86iNSm204uTuJrcPLvPCaIu9PCGrFaH0wTK67RMbdRq6fjLQcKaG6KOJu77bVQjlxYXSKujaBlMtCsPFhX1chXPab6I0aVDPCZuz0PV9dZFllsiia/VYDCC7J9/wCmGYhnYu+OjZ+c2ptuOtG5B0j0LRz5Wz5mYXXcEO3JNg1I0kqyJuZT0Oi4jtuFw6Dx4ojMGWp3zl4tEUiMINRmEscwU4e355pjBUqmF4yebhlC6gw2Nx91ebGQPO4jkPajfnfSF+Ik2ylHGRtItTVkWMJn2GTFDOTwQzzepezG3+HsvMQT5Ssmrc9RvLDlI3Nlq3aSQ9nbeapvdTZUXMwXRArgOUMLM8unNyrrpIVN36bHy+5LhMuD7ps5dbAb0VitavNC5jQqnZCY8VBBRdAAAgAElEQVTbxbkqIqoiIoxLhlf2aQCl7OLZVEGrFTadcH9d2+Cjo6zPYRG3z6y1OHdhCqVx5AgBgRPsRR20fbssmiN3uauDoAZCxnVAriUDt2jXPjDFpcz4TU0+6bX93rhAor3Rpg0gKSKUajg43OLG8fYsxUGY9n17oVjhCR2WW0X6QdUGZfmxMS5jxlXIIJyZBIsyoigjG2QVVWRZwt7xFvtO8wyceyBRpzVQbzLNqqhta1bb9BaPexnTq6wLVRHR7G/TVAEqXVgPKtD5LNfQj406jzi5u8loOr+pOMuCsdTHqNXcxiRmtTn1qIw5LCKOKmuBeOhCUSikuFg076OKR1Ig4jTENKhb5oztOLcBKysQxSWDS0ekj9lCGttFRH7jCroK2wl6L3ljDwrjMqaoAzZdYA/A5f6oTaj3TDNgg25UUFNW4Ytq66JvyAsx32+jKmo3ArkOiNvFPmxZRHQjW6ad+QAkPROKF0xd6LYDqcl1wK3xZpv8nmsYhjapvZvesJ/127wu3ydgF8LjFx5n88nb1kd4H1GGF8G4CufC8wNlyRuCqrEmwwXhUzey1dC6gqlyG4+yDKEMyaZn5wvWLnjFj41VKR/ddoVGnGmy7b5LXQeMqmhlGg5YwTjJUo4mAw47AsD72UJnFZkdn/cb+uCrozI6la/7IKBrxeRgi7g/JRraVCsR6DZPsZ4m1EU0i86dpNw+uEzRCSTKdcCoCtvN2EVwZ7xx5jr0ksLUNM3+S92KlxSPqEB0O986bHekAxdRCbQ8oR6WQiojGk4JNuzkSDfGbJyMGWWrfS4vBgMlqZuEQEr6gQ2oadshHXehnvklKuyCeHsypB9UbKUTNvqT9jnGRdIufEMmULp0is7zWU7Li/unlDAoR0XXXbR8LuKoCpxJrTt8wjZS8KiMOCwihmHNdly0/J3eVHpRLN7/MO+Ra8V+nqCkFYa7seZqb9ouMj5p+9Z4kzisSNO8/f54NCAvI6TSNuz+HCRRSRqWZ+ZggstHXTjmI0hzHVh/azTLEzwrdSDXklyLNsjGm151h50HrDbow/W70M2MS9VHI19ZQpvm4fs3jHRrZj8L3rSaa3WmAJ3mdlwuE8aL3+sm5sOMDu3QbQCVgK0I+oF5ICZkj9zRwm3v3iXZHCGh1QaLSa/VvguXs1t0UpByHbCXJ4ydv3kYVrPUpRVa443jbZ4dbTAIK66k06Vm4kBpGpfyBN4f6j5zwWwPE+u0i1cwfCCAXmImkmFNFJf049wmES8w08xdxwgOi5mZJasDRrVitGTtDyTsxAG7CTzVq1q+yzioicKqjQD1k08b4cxGas7E5P0QuQ5AByTOJ+cjAf2knNF9WRq1i8KnOCwyY9SNJG8ke7k9nuq6jfqrjWVu2SsCjkrBlVSSKM2V/rgNGGrNv46ppH/GYr2I7WTKYd5z/KywGyuu9cenFpajMiJRmsFkaIN2HHerUpqrT9xi+/XPr/QfSaVbs2ma5myVYzKnDXmC+LPgU1N8FHA/qFrf1iJyLVBSoF1N0lwLRrVNw1FmRiQBdvPT+PHqtPCetkJ2mR8ZnKBuJNcn/ZYW7ixsJ1O2kyn7zky8DOPKmnOzWpEGmkFQn9LuvDDscssumkez2m5cqkaxnUxZhKfiC2TD1bRkN16I7gaCjg8/UXWb9+eFcDfQZVzG9PKEXpG1qT0nk75lOdKKzSIi7mVtesoij2uXSKB2KTGHZYhuZpy21vqhlhJ2TIqEwyLhqLTjKFGanfT0c8dRSeDSO5LpoBNoZC1e/XvYUN47DDQP8/ovf7yiBaJfZMoyOpU/J0MrQKKwaoNNvJ9nEZXjLZ3UCUom5Frw/ERwVDUM1LzwTBTsJobduG79AamnukozUkcMUFZhayL18IQCeWkppLw2sCrgIZSm9Rf5gICzeDDB7lAv90cMnRYaODLxuesKw6SWTMYpiTItsbJuYFRLjkr7nJ5bchDlLZuNT4WYFAl1o5hkKVFUkW5cjGV/O5mSqGhOe1zmq/EJ2ieTfhtcs717l61P+fipc0Vg80+l4071UEHNxsCSwNsFuo/WywWiJ2monekxkA1bUcHA5dmtYobRDWisIKwa6AVmZQrGvWLb3fuojFpT7CDE+jFdGkUXW/0x/TRjpzzmuf3HuDk9mzfTB7Ysjj8/Ln3wyyrUjWTcSEIZM4iKlab0rchq6ZbIO2y19cppZ2ngLRazQJ3FNo2qCMYb6EYSh9U8WXsdMs2TdsyfhVorJrUnZBc2oliYltzfUvidHSF+HpSydHyDqCANUlSl2s2VH08PA2ZN3fZoCsS2lNEFIuOKKkTKxnJmVvPdoQIbxRkHFXEQtvlmXdj8OMids39UCPZyw1FVMTUVdROxFQYEEnYTa+LbSYq5hSJRtY26jCrCuETXwSydoZ53dHl/kv8bZtFsbWCM0zoDlzhvy+lczG/nheHG9rG9ltutetPxornrRmZ9ecv8PN635xn8Y0f8ba+jOcr6jIvkQlqi10DioGoX7iiq0FoyzVJG0x7jcn4DkbsAhu2NEza2j9n6lI+jLmUtgbfJziZqVoFG1Q3D/oSdImFchdzKkjO/A1ZQbEUlgzhvq4F4yjrvi/aaTWUER+Usb9UT0IcXGLtntt19fyfJCGTDfp5wVEaz99cho+9FBdsbJ63psBzbd60OdlstbxGeWeksE17dSDjrcyO4PknYy2M+6/LBqQATn9h+uT8iUJrtrMfNyZD9PGk5iu1GVJKooCVI7+YjdrXEURVRjTcJnT/aW15y56+8XIVsD0cro5O9MBy56iQwixL3lW6KOjhzE3AWlMvn9T7vzWTKYDIgUTZwaSud3Bdx+XkQIkCKyw/t+p8MeCQFopINm84Mo41o/Yd+MPngD/9/09gKAIsC8TwuwaoRVMb+TGvBfg5HVc1JU6JpyERhQ/C1ZEuu9s145hzt6tRpLdtFVDnBhnYLaYcoYBDlDKKcNC7mktB3N49agXNl23KhewqouaoRWp16xsWdrafb8vf0C7nXBA9Ly8/qMQgUibKEA2fh8csHpGNr8nv81S+cGQLfFYa9JKeXZkt9orWeBT7VTugUdcAkS9nenXHCC2UgaoASyXxU4dyzd/qmq+GdxV87SwdYHTzko5O9MNzL7QJ6JbV5qYlq2gW3S0rg333tWFL8sUDPE7GvwqhWBGXUmvfO0mJiJ9An9b1FHnnCeG/yTLAJ+KrN052Z18EKlFAaV7bKXiOU2qVSnNZiwc47P+SeHtjNlBdCZ5mF60Zy1LoalAsOm6XN5DqgqEM20umctqjdOBpVMw21cr69ztCf5Q+vGB+BbNrnDaVNczpLwD1x5TYb/QmvOd66J9/7i4UxFY2+/dDv83LGIykQpTT0krxdMLoDz0+wyE2csgpbP2I5TYlP7O64HPXaNIb7QSZKRloRiJChlq15sYtcK547vsS4TBhEecsM4n1Pq/xWSjZsu2oCi/lf28MR28NR+5kP11+sAuA/LytLrLxYCurwZIPjvNdG/I2qgFEtyTVkNdT3ochsDMbsXLt1oXywQGp6Sc5wcNqs1SU899pDIpszBZdQBpFqGm3LPOls3mfWTWdYhroreDu+LKDDdTqDL+z83Mkme0U0l0pgvyPoBYaBI2moGptLN64CxtWAwyLmam8yF6E4LhPGZdKajs9jN2rb6xZupc7YhDiGFa9heUG7+Kwwz3QTKM1xkXBz2nPCxgqcVYLqapqzFZWnIi99ZPEqMnUl4bO3D7m2fXDKt9qNavVz56ygKL/5yDpcs+DGlYsyf34yWPpdW9kkaTdC1oceMQx0m5rU1XwT2bSk712rjtZqqWY6GI4ZDMfc2du5LzPshWAMNOs8xEcOUul24awbNRe91q3Zp50fpaysA70qIoqDTWRoC5OuIqm+F2g0J2SEtaWc0mdYPPZd4V+fQuB3hbkOTpkqh70pW1vHDFzeZDHpMTkZtOTFylVBsNXG1RyFmi+j1E0U7k62birHcd5r883GVciktlGQo8qWs3oxiOKS3saYZPukDXVf1M4XkcZFWxcSmBPuZWW5WK1ZUJE3gjC8QL5aJBBpjZi4Gni5DbOfHA/PFYa1Ea3P0MO/o2XRl3WjePZki+eny4VWICGRhkFQtya//Twm14K8ERxVMaMq4Onh+FQu7UUEISxoM07zXNbrMqhRypoBB1FxoZJL4ASjew/dtJFcS6rGRh13+6arTS8KPp8fuUqDesvuHp967Xm2r96h0ZJi0mvJyzcmNh3J98tZ7DhgzdZVbYk1ijqYmwtFHfKhoy3yRrIdnRbq2sBeLgmFzQ/OGxclLE+PP5tqMvO7nqKn89HlAYRxSf/yEcljh4ioZuv6Lkc3HuPO3kOuv7v2IT6CEDYgossj6IVh1wSj1MxspF3ic+hyvBp9tikRvOlDEwpJKATD0IbZPz9RPF/OazIjUzCuQqqFsk+B2zF6lpBcS671p+yk07ZO2nYydSWYarbSCcPe1JoNO5UPfNUDv9NcNCmWLlm+rMK5yuNddM2CXih2tdNcS/LGCsNQ2o4OhGAM1E1jWXiwn33upSlPbxyxs3HMcGCJAzzhc7oxJr58jHBt7PKG+vcyTKfoiU/m1i1fatcX2iUTtya6eQ08cFXm+2nWlixq0Sn87DXUpg7IJ6mtd1lG7capi8oIEk77x1phuMR/VGvF9dEmNzMbcLUVNTzlgqd+4ajfct16f1RXu/TaeK5hLw/Imw0+dWPUkmIfFwl7udVQBmHFcEVEqx1nMzOsR+g074ugbmQbvOLHrYc3O46qYKlFwwfZJAuco3Da17+MDN0ftznFJYMoJ0oKgl4+t5kqiwgpGwZR7qJB49ZPGJizSRVGVcB2J5q8rEI+cnSJSW3H1WEZLhWKAHuFZTHyxAE6mOc9Pg9+ffHjLYxLVFog0hIRNai0aIP8HhYEBrGmbnsEYWYliC5KCXUy6c/VYfNh1x7apRQsM70kqmEQzhbHrSgkP+jxgq7RnfpiY92wl0uGQcwVlx/XrZlnIxTVqaTlOKjYjHOSoGLYmzIcTNBakk3SVkDkkx6jcX8WWLMi77BbFNiX2PE+Ql92ahGh1GzHmnEdMKqjznEvGCVHTeOeR/CWyzmfe/U6O5fvknR4Stvn6QjDubblEZMTKwR9oM3RZEDd2PB3nz7iMbh0RF3EbPQnbOY9tooYCF0eX+Oo3GywklTNfO270tBkiuLGZfLDDRqtyCcp49GAw9GwZeGJOgw4Z5ndYD4dYD9PyXXAoCwYlzE3pymJbNiNDdf6U163bROg9/KnOCoDqhWWg6qhNU8DHJWSm44tpXKJ/H6h7mpvM1Nh0IkwrRlXAVUTuXFHW7qrKCP2b+0ymKSURcT+4fbKYrQ7jjpwsUh2rhWT2hZHhllgkCXJ0C3XrDePjssY7XI1YeYrzuuwpRBcjM61/kWb5tHdHHVZhgKlbQ5qXbY+0FxLVCNaTTGrHZtTJ0I6R7KXJ/QDuxk8mAypjWAnqTh0FgPv/11m6XkxROy1VuA2qr0k5/6dNPeDACkfe2hXF0IkwE8BMVb2vMcY8+cf2g1fBB5JgWg6der8gr8K3k8wLpOZz3FJsrNdWEKSzkLe3Sn76g+DOOc4T8n1JfKDPneYVQzPtbZBFIViEAYMHE/kIM6hSBiGPtrwtJYxiPJWGPY3R5Zp42TA5GSA1qqtowYX5/Cc64dmRss1L4xt2oRtf8C4CjiSChrawBkbGRlQG8PnXap4y5Mf5/Wf9SuEGxOEamYamKsisSgMmyqgniaM726htSLtWe3Xl7QaZTYpeponthhuL5sRKSQl6fHQBhiFVlB44oB+UJHGBVFSnPJTNpli9OGnOL6zYys0ONOyJyGvGkWi6taPe1gkKwViqxW6HMS6keSO9Ntq/bPvPT0c89pLezx57QZgF92D8jI3psvTeebekTHkWnBUKhJlBfTojMCvXAdtPqE3T3pBkGvFMLS+5o3tYyYnA6ZZytHRJofj4dI0kTSo2UknbPVtUeesiDnK+uQ6oHZUgXdLyW7SnSN2fviI0UGct1YbX9XFb8TyDs2dj+ju+hYDx3aUKL1SU+puYuOgcr66WU6v1gJW+PJ1A+Mq4FZmWZb28pRBUDMIK3aTfC4Rv7pHasTzMC1jK8gvUGT7ocFUNPWNh3mHAvgtxpixECIE/qMQ4seMMT/7MG96L3gkBSIXdG35CMpxmTjfgU3U7VW2CoPXtgoXar1XKLYiwTDQba6ZNyFtJ9O2ekMgNbvxBjtRyB03vneDHgMlCSTs5TAMQgYDa9ZtTYI6cGwUjrlD6bkgoOFgwvDSETKokSpoNdnDk412AZOycVGHyxeMXpITVPO+VF9BfVKHZHXAILSLlY/O9WTZXZJnrx2G0tALYBgKQgmfvn2Xq0/can0fqGZWW7CUoCV6spBfmcVkJwPGo0Fb6d2TCPRLV7C5iG2KTJaS9mztRBFoJNY8Hjt+zJb8eaH/uvDC8NbHrnEymQUR6UZyMB2wl6fUjWQQVq32Uq9YALsm0kAYkE0bwZi5RH7fZ8PQCZSNE/qXj2iqgMv9EVeSTUYryOTBaol1m7xvzaiqiFotRcnTZBGjyuYe+gR6TwCxmxSMnU/tcs8GbISxLe48zVL2TzbJqmgpmYAv99VNk6kbhXb9dVQpjkoAKxS3oopBWLXll7wLwwszz2vrid+9VtjVaodh2RZYXkQ3p9ZHTPu4gFor4g57DJzeYITCsBVVLYdr5d7VYRG15uHtOGczyTobhKTVFrsYBg39wL6QUBp2knxp4v0yTMuY/axPVllzb7TCLPvwYeAhmkyNMQYYu39D9/MQiPlePB5NgcgsirKrHTaNpGxs1XavOR5Mhhy3FFHOxCMbgmq2mPpk21zDpBaEQjK4YD6QQvG4GPAZmwIwrSaQN2KpxuF9SIGc7YI9UbIXhl1MspSiDgmkbgWXN/+uwrA/cSWj6lYT83mGPsw/ucdcqlAaXtO3OWNemPl6g8atn6sCZ5oqIJ/0WjL1yAUAlY4Np59mNI2kcETiZRFxL4R645NZhKBKC7K9bW58/EluHl2aY1nRjWxD6xeF33mUZh6eoWbsktNHtWxzNLURthJD591EYeW0Jy8MrMk818om7XdegxWK3hQqWpOkZUuRLaF415QKEDYGOgJlENQ8NTxmZ+toTqiU1YyD1qcrnYWuqd3eW1A3dqM0DDTbi37bBUjZMHRCo1vrL3NMOJNaEog+ShgGTc64SNo8wKPJgP7eJfv8zlriA8rA0u75Go7dTUt3E/H0cMxmnHNcJBwWcctG5N/DRYi0lbAFod+wNWpZibwGPC5nDDfjImFUhYzq0xG33iUyiHKyImZDS4ppSnBSohJbqqo66dvI5zM2Tg8C4v6rXewIIX6u8/8PGWN+qL2+EAr4APB64AeNMe+/3xs+SDyyAhFm5tB4iXmlrEIOJkOOnJ/kohyBVSOcj0pSi/mFZhk2SHnDZsCnb0wIZEM/6PPc+OzF1ftAfCJ72s+Ie9kpYdie74ShT+bPirhl5feLvQ+c8CZXqbTTNBvCaUr/cAs1HbQpA8uKtK5CIg07ScVrNo4Z9icECxRx50WQNt2IxDJCTdO5Oo1KaYb9CfKcGpVnYXwyYHwyQCnN0ckGt0+2Luxfvld4oVg1Vmj5hf5GprjWj9soX7DvehhWhMJuBvwmKavVUrPeKlhT6myxHFdBKzS7wj0NajbjnCvbd51v9f76wAvDqhFUjY2WHQYNV3tTdtKJ5T49Jwp2mE7bvNmuD7sygltZRCB7DJ0g9JuV+uiSNbWu2Pj5KGubDzo/jpWEpwcTXrV51/qfJ32S8UZrFj/PV9xFLzC8YXPM63duszEYE8UlSd9aMO68cIXnD3bJ65BbWY+bWYJulm+uvFsCbC1WnyrVaIlUDZPDTUbjPsVDFYgBUl2534vsG2PevOpDY+tLvUkIsQX8MyHEZxpjfvF+b/qg8MgKRJ9b5zlBu0KxqEIOpoOlzDPnoWq8didb3splQtGaVOG1vZinehWv2bRVNBKlSeSA3JnkuhPBI5S6NZ1ESUHcy+aETFeAwIwU3PsflGwo6pDbk2EbjBNUVqj0N0dEvcxSlYU1wmkIw/6E5GSrU9fvYgvlVtiwkxSutFZ+ytyj84jszjZSNSSukkgXZuFZmgUSa6/Jgl3kptnZlR7OQlFGbeXxRfjUnGFYMq5CAilO1ezzsFyU9u9ubb4uEuZz3+4WgucmFU+kPV7nNGEZWpN5ouo2ECWrVcsXqw0rg22WoSsUR7VkVAl6ASROyAzCip3emGE6JYrKCxdPXgWfrpNrRd5yisJWpNmMczac9pcEFeNili/ZNWV7f3fTSAZN3ppPPSojuJklLqDNpjRUToN95niLog54bHDCRn/SCsFuQNmyhPbduORqf8TO9iFpPyPq+O1GlWXz8Tyq57FdXeuVXBse8/jje/S3jwk3JoTDKSKqkUHNNE/42OFOm0LjixzPFe52RCEeXgucpWFojg8321zhhwZT0VTnk94/kFsZcySEeC/wFcBaIH4iUGvVEv6CFYpeGPrq594H2MXYEU+3/5exi84DpWb0WqvgqdMSZc1H3cVypzduqaOeHB696GcrpmlbeHWZJrc/HbQBFd58GoUVVRER9S5GqK0bSdBhNPGh8JULqBkGTZtIbgMkEkbjPv1pSuiK7ubHQ07ubpL2Mxot2/JaZ6FpZFtTr4vI+brKImpNSo22JtTJEiHXxWjS5/bJVktFNggrBp33njrznqXpq1szuseiwKtdDl/g0ggSVbfmylP3rgTXpzXXxT7/+eBxrqSP8dSdy+hasXe8xfOTQWvKy5HkjRVu09otoEuE4ipqNy8wci3INYRL8uHAkzUEhHHp/G+z/LvWn1n6awYtGXdXmB3nPfbzlHFlSa5DiSMXqE5FSetG0ouKuWCYxeCvLtOLr/KghDW/2hqKMxIEP289uwzQBmN1C1/Hh1v3XCEicWbWYViutCIkyhAKmzd6XjkzG1gl3AZnnthgMXjJz+U2N3hkzf15GVFU4RzJ+IPHw027EELsApUThinwVuAvPrQbvgg80gLxPNSNxOtni5MmqyKyKuKojHnmpM9Hx7IVcKErPAyz3d5i3pQNuHECcWHx2kwyLt9jTUCPRkuykyHPffxau1u8iGlTN5L9k01qrdipFRu7dy/EENMteTOuwlYT6AWGftC4agezSfz8wS7TPGHn7uacOWt8MrD0eHVA/4m9e3pmj97GmGgwpRz3ONq7RDZJKcuI5w92L5ycDrNivKHUENgF2weKJNGM0N1rK2fRoQUuujhQNgJyUp9di/LfV/+B/GNfTK4/n9oI9vP5Bc7z4t4trECrXUBN3hgCz3N6RgmkrjA8q46g1oqyBBVENFrbNCNPyt1It5nyOXySUW2faTjpMTjeIhCGURW048F/vhU2pzaYgdL0oqIVpquEjC8t5YmzlYCt0Jb36gdVO8+6/r1BVNCPc6JoZqoM4rId270kb9OaziLFWMSyCh2LaPNGF1JALgIvyM8TpnkZteQhRR2e2qg9cDxcppqrwDudH1EC7zbG/OuHecN7xStWIPrkZC8U/U6/i1tZj2dGaRsI46Mqu9UIbF7S6QXQRovOSJBh5uuTHQ7VLjfpRVBMejz38WuMy4TkRfAbHk0GNI20TBhhfW71C7ALlTeNaacd9gPTMo/4eoceB5Mh0zLmyvbdOS2gLEOOXCBEunt4IYGstSRKNP3tY+LLxwTbY8LDAaO7W5RlyDO3r54psLroLsR20U+4EoxJ46KtMqLrgDTN2RiMGY4HjLIex3nq3qch7yyqXosYxDay2G9Qch0wXningZTgvvvTzU+SPf9FfNZGzKVofpX2HLGWFs+4H0d8oGRrnegWUfELfVcY5vp8c6vWimyaOpJ0dcoct1dE3Mq6NH2GQCgCqdiKZvMBfGUVm2YROiGx2PfyjI1bUdncw7ErnVYZ4XyRGY/3R63ps6jDufni+W1Tl+8aJDahXSVWvU3T+cLglSMIWDVmfERsWydyicDq5lcmZwj4VbD+1hk/sBeKPrJ8sZ/Kyj7zuIxXluV6IDDmoTLVGGM+CHzuQ7vBA8AjKxBXOduXwYfqdwXbMyebfHQc8syoJlGS5Axy7vocwRa4qNGuMAQbZRe5aNeiDp0pdXW7y2nKxz72FL9yd5dxFbaBC/eKogo5OdxEBZp+6qjQVtw3q6I2ebruJDIvwqcXaCMYVRHPjYcc5j2uDGw0I1gfXq1V6+M5Tyiu4nf0eO7OFfbylETplqHF16NbhlBqnhoe86zzleZasR0vVjipl1L2+VJKiTrdT9607N//IrmBErYw9OP6EiN1hYPyGX6q/hE+LN/Em8Vn85bLdpE9qmaE6bWx39mKBEelYXyOcLtXYdi2XSuyTJGXUdv+XCtu5QE3poL9oiYzmtxUlqweeJXaJpTWYuI1uV5gTYhnFQ0+C3WjXIRvcCq69n6wffUOrynDln6wNoLdJCOUuh3z97JWLOOpXXluUjLsT9hNsjkz6dXeBCXMXHpLP85bJi1PXn94vNFumo+yPnt5Ohc49aAhRIgKnnxo1/9kwCMpEKsqYO94i/3poF2czmKL705if76NJoXMaLJakwrl6q7dWzLubEffrNwhy86Oum4kWRXRq0LLPJMlKBcdOc1SPn58icMi4qBUQI+qkVztPovSTB0DyNL2yKYl/Z4cDwnigrqIyctojnnkovA17TzDTuZ2+V34BedeyIm7/KpKNcRFRFgFsKSYs9/V+78Tpa05Muu1hYmnpfW19uOca43iuZPNe4omBCsUfVoGWMGxl6ctmYE3551uH6QBbDchj9VPMgkOqPQERUgiBYnUpIFmVEfteBkoydNDwzBo2CsUz0/UUu5Y3fhozPPH5bgKGR9vs1MkrVbrx11Rh+xnffbyhL0iYC+39TwX8Sq1zWds2oqx3rgAACAASURBVM2PL1sVSpx2aNu3lydt6tCyCO8HgVbDq+1YWVWZJujlXL6yx9NZanNZOy4AH7w1yVKO854lPO+maLg0GZ/XeC9UbGCDptJexmMDS87ho7f9uPaapc/NDJQmTXP6m56UX3IyHsDDJvV2MKakKU/XC30l4ZEUiHUTcHsyZC9PqBvJ08OTU6zzHj5h2cMvblYrghMyS9BtFEm5QaJky2d5UQRLeENhNiG8j0C1offBKdos3Ui3S0xav82tPKBq7KS+6s5RsiGroqWT1xMHeK2rLEOKSY980iMrVgvRi8BTd/mw+FCalu2/Kwg9/dqqFJJF5GWEUraKvakVpgxoqgCtXemjRR5Mp6V4E+/+dMB2Ynlhr1w6IO1lRKGtW/fsaGPlfZdR3ylh2IqKVvD7BPz9PDm3Gr0SkEjJ42LAiXg1uTokJCZRgqELTBoGASOlSKQVhk/1Chv8E0ZAzPMrjAHPjux7Sy84m/fzlKMyZhiW7SLtzXE3s4hbmWCvWC0MXz/MCIThqIzYK2bpHV0Wp6MyIpwOuNwbv2ihWC2prLGIrIrO3GiJwPoUr73qhZa0vSgjpnnCKOvZqOYiWXkNbQTaVcHwbfFm4vOYakSgCeOSXpLzhNLt3NNacTLpc5z35gg4BsMxST8j8QLREQ087NzDGdbVLh5JgVg1lu/xqLS+iEGYLk0yXmTen31fOROU4UQeo50gTauIQdBj9xwzfuAYLnbjhEFYs7Xs3h1huAzd6FifyzWuQuqFihlWU3RCsT9qr+mDDjw1XD/OLVfiQuSmT2i+SDh3ojRboQZUy9bjkWvFqJNvOMfReh87XJ+GMSgimipocxqjuGQrnXBUxu2iDoDTcm0EoqVOC6XmscGJDcl3RZ8HkRU2FzF/+XP8PRJnXg/cgm2DYxIXCbnE5yQNl2K4FMN2FZCNrpBHr+dxfYVhOBuHyvmdt2NcXUTd+TGnrBO6gY+OJc8WlvzjatNjOzpbgNSOtm1cBQzCyEVJ2jSDW1nEXi44LBsCJ8BrKagrQ47VWhPZMHT9Zts9K7fVRa4Vhy4A5H6E4lkonKAqq/DcyjRSaZL+FBylgx+Tk8Imz3c3g6HUbCZZy6m6yPHr39cgtAxVw/7kTF+8F3hRNIuS3u74Hr2g9BGy3o2gAptG0q4V91k0+lwYEM3DK0D8yYBHUiA2hpaK6agUPDu21FI7ea9dJA9LGyqupK1RFshmrjN0hy6rwgq0TJSM68RSZ0kA5eifTvuNBlHBtf7U8jjGF6sm0N57kWJK2okXyphxHaAcl2jLrO8WZm/e8fcP64DNJFspDGHef1I5LS9wWiZYbdW3JxCG7bgEIgZh1U7Q2gUqeD9i6sLkvdlsEUdHm+3fUjVkJ0NG437LLALMmZaaRp4yiYVxyfZw1OaTarf4Vi4k3wZDRC5p/LRAjoOKoTNTASsXVB95uUpw1kaQ1cqV/ZEMwppAmJZnNHfvqB8Ynuplrj0J1fhpHgsThuHsurZ6ik2kVyv8cT50XzeGvBGM66b17eU6wQbvrUbdSNteLWwBXmEIHHG9L3oLMAgkw9C5DhrFtF5uqt5JcsgTFygiCDqn+eCVVdaZZW2rjC13Na0F/cAz8cwCUNrUDPdeffHk89BodaqCzSpc7o0Z9qbWdzcZkFXWneALdYPdcF1Jp+xsHLdUgvX/z967xEiSdWlCn92XPf0Vj8zIrKxXd/3ddKsHZgaEEOyADWzYgKaFNEJoRrNEIHZshgUbJARCQgK1NAtASC0YRgIJFiDBBjG01NMMaobq/ru665FZFZkZHuEebm+ze81Y3IebPyMiH3/1n11HKlVmpIe5ubnZPfec8z1qAXkloEofhEtUizFWiwmKKoDgLQQ27zMrlnEf7WEt8Xg3L/JtwvMEPP7svR3/lyE+yIRowxKbX5Ye0jbAudF/vGmI4WjBAAI8cML1An/k5lRQqFSHtCXgZK1tWSkPCW8xMQuvnQnaynA7KTwUleYbg2NKOqSt1qfMcffDfVcy3Be2gtjnlM5IB3TEJMX9YV0NRrzZq4dpY7mcQEkGyiSWywkulye4rQN8Mrm58/oQLrX+pmhwGmWo0ulGi3gWDDUkd8UXqFHhCag8KECgFEFnFt9jZrrSJMxceqCeh7bn4J4W4bbJ8CKQ+Hy0wtPJAnXLIfsLVEpXKtuVVUD6ez2Va3HpN18grSUTrIPDIBlOhZYkU72VhTvctpuKBpmRWzvmOwhgZ37mzsVU2kP+pd6U6jmeJa/fF1Fsox9spHQL8njyPIsynE2XSMaZkw6EsSGzm0UG4CIs8Hi8dDZsgPaR7CRDk2lvxsX8BKt87UCzDeJ5ExH+Y5Sbt42+r9HX37634/8yxAeZEC0X2QJjKtUjbYF5RQynq4PstD8c8zwE1AP1mFPl3xccPpI+RMB0Mr2pbXUGpC3BWSBwNkgisakKGVGO9H0obFvE7gKHYcW1o0D7u1WSG2WQAHYxPAtqnAUlGF0brVovRcFbrUoymO9QJp1h8MZnNPNUXUUwY956dwvFSpXJjjh3gPvsZNNMC2vPVxN8s5oYlwqJp5OFW0SaI7t/SjuEfo24bDGvQsje0+080oEZkMuxBXR4jk3Dd1ROpFq3qgGtPrMvWWvDZH0/BNRzi5YmlWsZs0fJCqezJZqG4yKb4Gmk25OH4ijXsANGrIciuq2Z9FZsgK5FI8j9kKZyj3D2iOtkODJi1YgIAI4RB86CzefD3h+2VWzdPvYlRWs3tv8zGYUetSbkB7R3G45hMnSbmAEI7Fii69Ruh8EGIwqBOUxCasySFMk4gx9puUTrq7rItCVULjli1uLxeKlbpcajczgTL1YJlsuJ0+Zdn+Pbg2PeFMV7n/B6wPtphvjhBfV6JFxi2RIAnrHN6SD7Hq0hg7W9guy5MWelBrJ+gJsEH190n+IiXCc8S5quug7M0+ah1m3b6m1aKPV2DFuDVm7KksHt7GvYNiWkQxhWUIrgCW4AwMmKUWOCuy/2kaCtdFtThFjdrFuXjChnlWMBMvZxXrcg2UGQA/N6Ld9mNhSWRE27bu9CaK9T3Wp047LheF5QcDKGz+QGjeXQ/EkpgmWeYF6FeF6EmAndBt14jZn3FY2PphGgtENhBNFzyV0VK0S74YGpFEVhkqGtDDjRLu7rxZti2VBc1QRpq/mZATVVngnL0Xxb3VTu9eDEc2LSMeu0JFtCAERaQ5SvUc35PTBLlkdnv7NlO0iog5jyDvHYw0y0SJhE2xFX3RxCXtpNUmU2cT5rd6vCLVqNbrHqz3AedAhoZ2bTYoNL6LMWkDDUibuXsE4ytLVAUwtnlVbWPnzeYjZeoW6EA65EYblBso/Gej7LqEJcBcjrAKOw2HD8GEZdhCiLEEUVOJAbsPnMH4pDSfsXFz+Baj7QhKgVVALCD0pX2WBH+IU2vug+xWex3iGnrU6G26EdB5hLhoR0GCf6YSrK8CCwhNK10731b7OLAPV6/TPRIIgLNwN5ghskonKVIwAs0hGyxrh2mAppO+JJiuhsAZaU4JWGLK6Mogw1yTWgCtLMwABLYzBD/gGCczu2lT3ajjpIPLC7ObD8KtlpOHtrFsM/yzgYmeIzRTE9wLHsWr3ArbIEXy5OcVnu16S1UHkNrInhsxZVI5CWEeZFYuTIrF6khACcoPgqj7GoIjcf1eooFC3gBKAvDQglazXCUyfETeGGkKmDbdk3iRHX6i0zvzGbEw4kBIX0NojydyGGKdHnNvNrxMzaewkA+3/PJkMbdqOxL6w1mo1bo2M6vqcd0nnQ4dxvwEiHrOWYVwE46TAxXRe7QSrvoUXctdo4uCxChy611IvH42uMT27R1sI5oog944BonIEyhbCqEJXBG7U6f1nipwrxAwxKOsz8CleVD+rZtiiB7IHWQDS5d3w3FtAeAaH4XD7FZzHHryRamuvc94yaiG7HMrMoUKIrgbrlCP0as8nK8YlE3qAsQsd7svqjUVBB+M0GmswiRO0cbwqjthEX6KyLuKKIwtK5YABAeFNhcTtGWkaOXzZMQEFcIJikELMUZFKBlgyq9PXO2bgv2PdWTlmn26hu+EDdx8baE1K610lT4badTiAwgJfR1oJowRLD41UK+DrTs5uPew+nUYbt6I3dz1fzxweTIQBnhLtouCFUT5yD/bwKkLbMEe6tZRUajjSPcZ2PHK1ifW7U/f/lIBna4ETfN9So2gD7idwBlYhZj4J5zkPvWOhqqzfC2R1mfoMRb9FaQYmeu8rOaZxy4FBys6+zJsqJX0H1HkYsAN/aIFpPQyt8YIEsxzh5zOu3UNsEC0MxiP3q6Jxet2rVBmI3azkuiwgBlZgEpe6Y2DFEGWuloXh/xaatxUJXFX6fTkG9HhfJLcYnt4jOFpBFAO43qPJQP4uDmSAxLXTmN+BFCBHUKPPjAvN0T2fmzurQ6MkOY1ipcqKcxup7C0/A8z95f8f/JYgPMiECupoZcYkTnwLwXKWYtj6eV8dneoD2Smv7EV6WHJ/F650x9XpMucKIrVVbZqLFF5Ml+EC+61BYTcJjQgHAmtd36Hi2/Wcfmqbh74yvpFvOrasyhhUhJwpngcSy8e/tEVgpBiY5WO1jFBU7xxyGnskCl6VwXninJEVTC8hKgCuCJo3w/dUj/Hw1wsvSQ8z0Qpq1DAljRgCZY1EHmBtghK3yVB24xZyTHmkr0HXECVzbZJhL7igVstNWX9IsRvPKx1VNMDdcvYAQtJ2e2akOdwE98cWT76F6D5dFfPyFg7gIK1DPx3lQ70gM7gs9g1sn6GFw0gPdcRi/BdjIA5Wm/e7vO9NSvYfKaHcK3t6vhbjFQ2w7utF6HUUFnj66ggj089xJhq7tHG2hVwSyFiiLdTK8LCIw0uEiuXXHZVEFFlV79Uit/dK+sNVkpyhk7YNQBSW1EtPpbImyDDZARNuV5ZoPbBScrKuLUY9iRYMwLlGUIcbQBuQBHR29Zm8VXY2+/Pr9Hf+XID7IhGgFPbTwcu8G9AHpkEsC2fm4bNZJUc8Rd+PzJEdAdneDIVvzw0a8udNI9V2HdbmvGgHcanK5Nfi9Syz4vsGJAodOZrnkrm06CUq3KM3Lh9j06thOhHSrvbYvVEfw6voUADCufaxuJniVj1Cpze8tlQRpGuF5ETopNAA4Fbs76yEI5DofQcxb55H5EKFw97k6YF5pROZ54GHK1U77cgj8+PWnL4AfnuGyiPWiv3UNrCuCGvz8SVQ+yLnBtm+HAB1LHbgLtNN26w3ffTc+x8Lqg+4Toh+CTVQPzWtsueNHAhrJOuxCMKpwdnGF+GKOdhUjX0zQ1sLQK4jjqxarREsG2tb8FjVkGMFsBVkEkHsAZ9sRxqXrKuS3ozUiFbobw/0GQS2Qr5K9YBoL4FKKbCRK5jdOh5VFFXgRIgpLR0N6Xx6eNn7iIX6gYZ2yA9Lh3JeuBbNsBNo+hFwJZFIddRDgRIsLD01DLYpyFhSYxhnGSYYwLqEkxXI5cca8v6goa3+niryvse++4ES3hi3YpK3WAJ9EaBSebxwhOFGYl/ercuy88JDB7yE7o2G8uj5FUYZIi8g4nO/fyORSt/Q46RGQ/uDibxPxvIzAiXJ8s7cJ3UL1wD2CqRgARrY3AlTh15++wOlyipfZBHPL5bPJ04lQE2cvZSs9qy17n7D6osMNgfXxPLQRsXSlQ2a2x2LoFQnsimXvvNeWrJ/lWKYtQ2jEHwKqEDLp7slRkuP0kx8Q/ewS5NkI4e0L+F/dIn1+gbYW2ljXtB+bWgNmjtkm0bCGR3VlScMaZBWjySJ0ku1VVLLJ0NqotbVwCVEENUYXc7CkhMxCcL9xQvTu/QZoZp0U176HhEt4QgK0g5dqor7wN1Hi7y36n0A1H2RCpKRDwFqETIKTzd2eXRhsVcg8D//4TOFZvDnfsujNgLV4kU42WkQBlc7OZti2jMISvmgQRqWzKgL0A/M+BvHLPMFlPtKiA0GJx+PlhkScLxoI0SKIC/jxcUADo8q5Z2wvXqVZXBLU99qhMqoQPiDBaOBO54SzT0SHi7DBbIuuIjuK+WqCrPENwX33WC/LtdLKr9yju2R5eJf5aHPx7qgWH9iTPDRy1HxWz3OAmkWtUcdZBdi+6UW4vl7b/DPLh7TXe8Slns/1HhIut95vPctlAJ5OFliVESo1BfcYqiPzwjcNm5wZ6TAJ9OI/L5I7f8+2WR/qQ2ifMU3OJ4DU3ZjtoMyYW9MevRDwONMJjSqUeQhKO9MCp64dKbfmwdthE5E3UFvqFD0oMbjdXi3LAFUj8Di4cqbbVjYuiAsotVsp2hapbYHHs1uIWQoMkt99JQ7fWfQ/VYgfXFCmEPsVgurw8Fv2PQJK8JuTHr8106a1SyOKrdGWEh+fXmntwY7geTqB7D2nPGPRnU3DQZlwvL5knCEcp/BnKfioQNcy8K2WCoCjs0ZbkVWK4o9uzqA6grNstxKz8H9GOoS8Mbyo9YMqfO0RJ6IShEvISqD4/hw8zdGuYqyuTlAWISjVljPWyHVfPETYOGAtYr+Cz1q8zCZQvYdl46NSer63bVtl0asjrlt8T8LK0ScqxRx6Vhqg0ddpgh9Kk1w8YNnAVYuLRj/QH8eatG4J7MHx0e7O+VCqk5MV7R6GVpTRf2aeh6mwwtYeripvrwj3fWMqWnefHZrxfXx6hdnZDaarBPMyRtZypJKAe/3aFqozn30gL2c5ptKo6iwNmEr1HhZ1oKUOzXqoBmOHEW8wSzRArJL8aEtZGeUeYHP+GFAFn0kNojKtvzdtAWZpAnz7FCcA/PIaahVh9c1TLK5O3P0/pDCowWaWkx6lpPg+nWL28hyTWoD7DZi/nkPWRbhhMnyf2DcTJ1yuRSSMZNve9ilT8KMSwaP7WaK9tyACJPjsLQ/ye+/iTH60+CATIqEK0/EK1/no6Hzq84Tgr5xe4zc//QYA8MPrc3yfTrVN0OkVzi6u3E6w7She5olGCA6k2OpmXf3F4wzhOHO+fWTSgTY9omyBthbALTYkqO4Tacvwh9fn+Lzx8Wx2vXcB4aRDJGok42wTmeY3EEkBPs71DjoL0aQR6sUIt6/PNto4TmDcXK+HeDQOIxGVJsuHJUQjnIuCRWxWimIq6g05O4ugG7EOz+ICT+IUkai1IWoVojYVaqUYfr4a4apamxRzsrY8ymSHgBCcBUP6wd0UBBu2OhxWYgDgQ24seHr+qDmsM18jP7XijOGz1m+HBNynh2rjs0cvcf7sEtGTa8SLEb7IEqSXz3BVb9lYEWgNw0FsA2Sy1l5Xasyf12o1gL52AdW6ntPpLZRkaFquBQuOALhsdecqPiP2HpguRNcRSDxcsWnj3NME6k8/wSSNUOcRFvMTSEXhi+PtRUY6tB1F2nI8vz53Bry2s/OQJHjfIEy6eaPdOFOmvUht65WPNcXIEz9iQuxq9MWf/Xjv/+cgPsiE6JEeYVwiERUSJncI9/pB93ARSK1beLJEpyhmZYCs0ZwpXzTOcFSI1lU11sBzSDFoGoFklOmdZlSBBg1IqOCFAmBmLjF4+K2jxkFuotUIHSwoXy6nqBTDJ5MbV50OX8+o2tnVEtNaokEDT0ioSj+MN5eP9u5UGV17+lkUbNb4YMRUY1aE2rb8trQVubH7CUTjoOf2d4bfgf1c2xqviXF9iETtWr+J0JD9SlF8tYrx8xUBI8BUGBI60UnRVoYh04lySDDf1gXlAwWe7cp3+/Ps+7OtNJinifLc0CzOfQn9SBGc+D2m4v76k5Zqsx3DtmMiKu3WMSpAxwW4IhgnGaaiwYj5ju6h9Uc3P9ehmWHWMqfFapGp+jqYz2q4sJaOIPiaprHdDrdAs8tSO7JYM2VrURXQAAGVGpQF7NAvPh+t0HaTvRsYew2HVKKyDFB++2zDKsyHBi/RO1a2SukNbi0ZfKYl/CJRYzZeIRnvUn2ORTy71bJwku20UgmXQCVAmUKnFFCvnVQIVXqNsWvGVnUoHi3dz6QxBn7v5P3+pxniBxs+1xyqISgmkwxt5xmtRvlgAIpVWLEP4Js6OSyqNfgmEI2zg9mwSiKdbjMaXcxF7WNSRYiCakOm7SFR5xGaRqBqhEtc20FJh9lYe7gVptW5nTi0zJzmH+aSa2oFPZzkASPe3HlG/JohdAn/7mu4bARuGoJlK8EIQUC1+LQ79oGcM2IdRkZwW3ZkIDUm4TOJWjKkBwje21JhkUETB1TtVXQB4JLiuS9d23NbMchuRizFA9Bei/toGEMgzXZ4tAOlnXHuCI0RtZ55qoavNQzvCC2Npv884j0KufYEvap8nOUjzAxa0kqYnUYZlmXs+KbBwEoqoApfmxa/6oDKCH9bBSbLP60HHQkA8JnEXz1/jS8Xp656BfbTQ2wSHIK0COmQlyGCgW7vkIi/L/S9TTVoyfw+pQphXLo5JPcbdIo68IyleQxjfH6D0pD768UIwoxLmmz9nNtkdp+E7T77LMNoXECuIqjSd+jZ9xJ9D/yEMv0wgxg5tETUTn4r6wgWpk2oSdRqr0/hXSEVPajJ+JCYlzEyY19kg1FlCPCHH+INk9F3rG3o8xajOMd4pnlaaa7Pse32iGSb2aV1xMgaH4nQmqt3AdftQmr//C6g/cPghsxuq85hWIJ37Fd6US5jLO+gWgxNXK0d0yHi+5OwccnwEGcwzWP8cDszx9avmfm1Qc9S/fuD79a6RmyTtymTCFjrbJw46RxhP2uZA8YcCztrBOCk4exMtuoIXuQJkptT9/qLk2t93rzFqoxQtgKJX2EUFg6cJTuC58VxAjuw9vkMeYMvPvlOz9O+a/FHN+dY1MIlw0OUjX1RmQ0fAHd+b+r12SmKugg1QKcWB9uxhEuMLuaoFmPUeeRQru5zmgqyMSMWJdlaDOJAkFDBmwhAcJDbFbobBpU/xIn1gUF8eMHnb3mQf/hOTuXHig82IQJwVVTIrBwaQy49B8e/y9TVRjxJMbodIykS7TRO1VGh4odE2gqHEg1541CHbUcdKZx6HoLew1lQ4XTgeRiJGkEVPYgHaVVxDlWHUaDbchaVGgUV/FwerKIATcewIti15LDgzu3j6wV+nfhsUg+oRGC2zAd1Wb0eEesHgux3fE4Cg1zd0nJlLaZhjlGkF+/t9rO9Z2yylooiMALr9prNggKzMkTCfZyIzlk+bftr2nZpwFrtgEK1OaxNhpXSYua2Ha9npT5KSZ1WrZbyMwpFHdkRsaZMV+o2GWpAkHJi661cb5wCqpBZZSWv30gSI7Zu7dp2q3WcWDYcL9IJno1uEfuVm7UJoTcJTcsxinOEhoYQDXQ+ryoftCNQnX7efCY3rJUWVYSzKMPnz15g+vQ1CJf4FdNq/0fzx+46DL8fIRqg2TUGDkTjKkcrgPFQd4x9MVSmoUxuiHmzqAINa8cdBIBuPkOxShDEm7qoSpG1FZQ4vPZ4ogM54YAI0J2d659FGSi7Aa7vJ3/3RtHV6Is/fX/H/yWIDzIh9p2Htta7xNYQcmW/nqtQD5iKtdyYbY0cavcFkxSffP7djqnvcHbxphFQhZkBkdgKRBpt0YAGqMQ6UXx8erXxu+M432jFdYpuzBC1cgeDqgQo1lY4YVSivMfu/SGRiBqJqHE2vtWWOIZftWN9RTrnhmERvdTrcRaUbqY4BPTYeas2J+7wJKTGsmu3jVYp5cjX+2yRzoISp3GKcbypkTob6U3GEPlLSXeQuxaJGp8lKbKW4SyonWHusDVvE5SdmcVGak9JivlyikUdIJMMFwdEopnXY+JXSESF6yJB21HMyxjRYob4ZQaPKbSrGNfzE7zMJnt/H1hLtFliO2DBNQToNPgoMTxFmzTnVWBaiXpGSntgXgUY8WYDUCaCGqdBjabyHYUA0DOyi5NrqI4gZDFKySB7D5+Pl7iY3eDs4gpKUsSLCaLVGGezBZKT9bzMjws8vXiFWnK8Mi1lq+s7SnJMHs2R3UwhlxPnhiJ46467mJ9oD02r9XqPOOSrWOahq+o0hal0/MODx5JaO7WpheMstrXQovIGjKSUVkeyz2iviDtVchEBjAJSwSsygHF4RYm+6IAjNlxvHX3/k5bpj30C7yM6pUnyyzxxnDXrNE89LY48Fc3AHFabhx5LbiIp8MnPvka+mDgKRdNwoLlb1eJQbNrIaE6jtWYKRAM/13OYKKjgi2YvECYKKlSNlh8r8124uJ1hqNJHkxoPvrg07Zt393BxojCNM4fyo0yCqnU1AqwdELb5aYwqTAJdsdUth+zWO3z3GsNVnAri7IG2o0WHZduBE4KAeggGMzTL09yuCG2MomLDrkd1ZEfXc3i+iV/h81GGmLWuss/qALnkKCVzyTDkWsQgjEqE4xTSuCwca3Vzolutk6DQtl8ddfy/P75+hOt8hCc/XKCsffzRzTnmlY+LsNrL+wtoh5lf4yzKnMKQ7AgyycDM2MC2du1clZMOy2aECtqf8C7RhCAuNIrahBVK/+jsCv6ydd/jxewGF5++QPTkGr0i8OMC4Y3m7A7l0QjtEI0zfHT+GowovMwmrpqfPXmN6KMrENohSxMnV3j++Arjz37Qmz+mMH95DkI61JI7M+GhNu1wLql6DzBWYYeEIyhVCOLiYDLsWrYDiimMdrFLkObeHpolb1y3oAFJWnRPf0Vfhx9ewCtKACX6qxxd9h6TofsgP80QP7iQkmGRjTaSoTXznXKFEZeGtK8Th60QAV31DcEqVr3CKln4p7eorydoihA01wN4e+M/JGZBgYvZDSazW3C/2aRL1ALCkPktr1GMdKukWoxRrBI3z6BMIgFcgqvyCPEk3Thek0UgTDqkGmBsoAxI4G0SIzWUD5u0d/QaB355Q8sqq4iT+BVmSYpRkrvNwCpLkJaR46zZ0EmxRy7XiY56m8mxUgqLBogZwYjplvNFnOHxeInT2dJVMpYTZjcZjCqM43wXrLFnBEiMm8hZmDu/Ssvjsg0yaQAAIABJREFUZFUEIDAVzbo6DOISLGhAaKetvrZ4hmwLxTrxK4zjHGGoZ7K3VYhlFWJRCywbgRd5grRluG6o8eYM8CwuNmXATMU3MfM9CzaRikKW0U4iTES1RhKTEZaNVqwJ4t3dh+XuEfNcyEpA1r5TigG0pdbj02snHv/oo5dIPnkF9rkCWgk2LiBeFW6j5q4vlyCKYHp+A0o7+LxF3XKcnt0g+ugK7FmNCFeYLCYoLgOM4hyjizn4xxl4s76m85fniNr64AxxuClRvXe04yP8Bn5cOHpEL6kDuHQtA6WNdtbYAr0UZegsp4aWZjYsWMe2YL3zGM3pM/QigmhqkPnVLy4Z4idQzQeZEFVHUJn5gSUlHxuq24E3AIS80Yr+iu4Mxo9FUwvwWoAVgU6gZQMiWqDpnasEAKfST81MaZ+g8LEIZiuIpHCu3Da432B0sn9Ir/Ud9xCHqdqo5ADjVCHZxmeXDuSxe67H+IqUakePM5lB9Q8XJb7v/Oc86MEJwVWl5fgq1aEyn+lJlOPj0ytMp7cbv2OJ0sBh41Zpqob7gjmOXQubJDpzXmfGxeMsypw7ibPfMoIANpghtQOmtac8pJIil1oIoJTAsiVIGnGwBbsdzOudNNy+eSz3eiyaDrIHpi3Bub/5fShJQSgDMYnA/mybFkCp9vIEdJdF8+w2n8VOro9DuFwnGvO9jOIcohHwo3KHp5fXOiEOg3A95xtuDkrJjLrR2sB5GG1HNDisI2tPTJfYtSPNQ9aDjXMsw0ELFzvYg05R1HmEsBJgTQnSlOgAeE0N5CXUjd5wv3eOIvGB6Fff8iB//E5O5ceKDzIhvklYZ2xKOpStwNevLwAAT/0GHlOg9DgibJUlWGUJ8IP+exyWCCPdmlwYAW4boW9l396sXx88WkDMUlSvZ06IWCQFwk9fwRMd6hcnKK9mG7/TGkWO3c+tQGXniPlpEaFpuasaXy5OsGx8jExbcKjDaF3MbSW3D6fpiwajsEAltYGynQm+65iKHgH1cFkwLFtprJJaPJtd7yRD99kHbbphUrTGxZVpt+GAM4n1fAT0Bkd2+yuMuhGo8sjNkgCNWh0mQ0YVQt4glFwb8G4BSQKmzY+HaNwRowAYrirvIA1kXzCDYM0a4Wae1OshTfK334+WN1yr8qjeQ91yXV1LhiqPNmaHw6BMbiBi40mqXRyoFjAYRqcomiLUajGm0gR0QrIbtigswfxmQ9bsrrAqU5ViuKp8hyHgRO1tWVeK4rpIQEm3cT9MDOK6LkKQhdxs796hLDM0wj5E/7Cb4uXXH2GK7+Hj9wHB0b0s0M6TX5x6jaqB7E9+Me/15zR+SohbMayCvn59AUoVnnzx7QaK7Fh8NX9svNu0+eoXZ6/2v88gsdw3xCwFPSk1FLutQccFmtdTEC4hPluh/9VPAeEjPPka/OsC2XeP0UmmIeOVv1eOylWJRnAAgH54l1MAcOLddg644XDeUdzWwY4Umw3KJNBwNyOsJXeJ8V2FpVcE5lpywsAKhhHv8SzO8OTpS4TjDLIWaM1/G+c3eASUaZnZxauW7E5SfdtR0E4nw0py5xVor5NNkk3DURehmz9bEQSbDAnp1kIHWwIIABznc+bDCX1XioGREIDQIt73EADYpw4zrwItoD1IttTr97rA1JKjKMON4wjROCskytRGlSiC2hHQtV7obkK0IWt/L/Gc0k4f556JwaMdCJMQfqO/F0WRSwJK1mLnh6JSDK8zvYEdxzmEaNxGsq0FytUIfmSS8z2i6wjyOnAArbs2g8uvP8KMPjd/u1s39t3GT+LeH3RC1DtB6uZXsiOOanHXQqcJ8RTfvL6AEC0ehV8ffSCbluP/ef0Ef3AT4GXVISAMUxHguyzGb0yX+PzRS33cB4BwknHmHkaPKd0yER4gOCA4vLRYK9GEHF2YoBcBSBTAC3OwoEGTbYoVDxOiXbz2yVXJjiKrNzlPw0VwlOg2VSX5xnxKH1e6Y4ZRuV7YK11V1m9529lqaMQ00Ga4wD2LGlwEBE+iAr/56TcIxxkIl2CwrePNBZsyCaX0dzKE61u9ztM4dfPRQ7GNit2OzrThyjzcqDxsMnS6uK222VK9d9Av0yZDy1dlRmicHu7WHo2sZVi2BAENkIhN8XZGAHSe4XNqJC0lnZNvs5UPJR3GcY5kpNvA9ru30mT3GQtYYJv9bvbdk29SKamOYF4FuKoJzv0OHycVZn61voZ7tGqloigaH1FQYWJQpZ0ijpivJL0X2rRp+Uan4b5SdYuvPkY4zsDHOfiogBc26I8YYb+r8HrA6+7eVH3I8cEmRE4UpEc3gBx2sbFtu7IVSIvI3ahFFWBpKiLrtn5bB3g1P0U8SRGUKZosQl2EDo5tE1zR+EhbhkoBsuvAKMOIAzO/QeJXbo4C7E+Kdl5S5aET3D45eQ1Z+5C1D1Y26BsGNA8XEqBMQQT1nUTgYfis1YtyPjpIWh8l+Q76dShLZSNkmobRbCmTAHBJVyqKQDQoqgCrMtLzX0U3CPwb7806TA2Xa1uj80lU4J/4+BtMHs3fqt30KFnh8Zkmod8HeDRsl2Ytd3Nrn7UHxQoEb929IRVFLTlURw4unkM+aKUormqBq8pDQD0Awm0OFqYSlh3BH16f4pNay6YttjY5+4j71EjTATopcq/HyIhc2A6KVWuyVBU7ex4msmEy7CRDu4rh0Q4MGfqGoLkeo1qMXTK01fO+Nmy2SqAkxbRl4Isc5esZXr06x20V4mLvlTKfr6NO3D1mHb6Yzd0cf18Mk3wUlvCNzqgqffC6MQmRrUF4hb6eqtTnXt2OkK8SpHmMVRltCKEryQC23iAB6+fEXjeHeK8FOHKQSQUvBNoVA96nSg0AUB+Iv3jLg/xyGwx/sAnRBicKyQDC3rQceR24hXhZxnpmYBQztqPtCG6rCIurE4S55hbZRHgIoh1Q4kSfR1w7P9jW0bFoa4H59QnisMT45BbhowVU6aOYz1DdjsDHOYh6uNEPoereyZASg4I0FdEhoAhlEkFcYkQV2i0j1GGFYP9MqEJUBo6HNYysDnT7sOzcbtomk7ajTsnGzs9sMrT2UKlRIgqowufjJT46u8LpxdXO+xwL2y61n9cmw9mT1wCAcpUcNHs9FNo2S7eUhdrdyDCqEIUlAiOCoBRx96cl8h+KrOW4qgVelvp8KwUjeh4YofG1dVLbUXyXxQiZfhaOxTbggxOdSGLW3nv2a1vz2/e7rASw0OCqXhHUi5EDfDWVPxAd0MvSdpVYFiGar58hHmdYzE9wle7yLw/FiHX4tcktPn56aWhHmxWpDSWZc7oP4xIiKZzOqKwEKAvdbLQbgNE6pWef+e0IqyzBdT7aOxqoJdv1xhxcq+H5EC5BRh7AGei4gFpFkNm75Q9vhKqB9Ofv7/i/BPHBJkQrKwYA41AnQyEa1/pSHUFpdttWT3JfyI6glgwvb04xKgs0rd7By446XhewbpXJDgiIRu4FhotGSOcS0nDxE0HtHvq2Fnj+4im+X83wW8++1aK/40LvqNMI5WqEdhWDzR4mPGyDGAGCQyFEi9oo/w+VRnzeIpSNu1aAngv5kV4sCJcaWcsU8tuRe6/hQ06YBKFMo/UOzG+0PNy2CLtNinrhCahC23kYcYmZXyM2bUX7/49Pr3Dx0UunJELuMaNVkrlkCGjAEyUdzmYLTB7NHcy+M0TqbLV/ruOzVt9z5r6aVyFkr/VnY2OHtR2Ct4Y6Y4TZaXcv9aNlIzCvOK5qsuEJaZPiidBJDFjz7V5WDMuG4bOE4+MDrT4HprHuI5wjoDqxDoXaj8Xwu7dUAovO7CSDrAAsRuhatkHTUGpdNe2brVuVH6UoLn+4wDLX34PPJIRonQfhdpyPbvGk1LSOLx5fOp9S/f5i55mwCaksA/0ZjIVTBwZivis1+DyESV391ho4tbgdY5knmJeR6U4pJ0JhN3dDJKu9Zs730LRlud/Aox3AGRAF8ESKXhEU8xkejte+b/xEu/hgEyJgZjREHd1tDyuRYViYtl6EdeVg3ektutLGofbWPng3ZZrg2ym6UbVlqwS3VYRlI5DmMc63z9Pynga70r4h6Fptjop2tw3UbZmdHgsR1IjU8UFU3XI0jVbfOBTDdpd9z05RrG4mmC9mDrAwDKuFWkrmFF5sDEXFrQxbwltHegd0MvrskxfwBwt9Jxk6CcC0DjtFUQ0kuOyi1jQc+RaPdDZeYXp+47ifx8K6JAw1cacAkjLCV8sTSOMFmdQBRKlfY6vAQzq69l4D4NrIWRMgbQWylmPRaP/DfQbJuQQ40QCSEKbq6K39kmeoGj5GXBo7rv39Bq1u0zulGwDGyxKwvHILJEqESZhmdnysE9JJhiZjG2LZ7nNbuoNSoGyTDmWVj/IydNfGXvsgLg62xmeTFX7d3ENhWKGtBZjfuGR4cIa+RVWyidGC0IZhwVpNw9G03MkY2nNkVAGKooW+n6+LBD5vIXItYjFE65ZFiLrRx2J+DTp+Dk+kaF+NkT4/1hx+B9Hjp4T4Y5/A+wxm7Ige4rumeg9py5G13Mwedn/XzrcOwfEBnQytluX2+w9biW8cZpbYSwpVCfQyg6da9Dgu/utmOls71M60iIa6jcNQvadRoo1AaHawd72PfY/F1Qm+uvzImARTjExCe5PQlUsDn2l3glFY4LOf/Rkmf/kbAED5p49QLTaTbrFKNigAtjVXlKFTp7HJaZxkGJ/cQiT304yMRO3QiE6hhynEqwRZHeBFnuhZdBW6+dsxc2gbWaOTYMBaZHXgKs5KUedMcZ+Q1vCa9ADXgKRcEnBCkUq64X9og5IOIZMYbVWZAAxaku+8HtgPhNkXNoFsC5XbUGq/iDnd2rD5rMU4ztfV1IE4nS033psase590RlEcNPy9Sb0QNzlZj8UodAfTB9rUQfA7WzD7YPRTaOBuhF4+e0zdJKB+Q2adyy1eDDewtz6Q4gPNiFaftdDBLiHydASu4+5MGzP10KmkHB9SWO2HwY/bCO6VpIVuTZcs3s7cCgCWQR6d9z0O/926L2PhYXP61biJnqy7SjK2kfccARmsdi3Mx8ujKubCb66/AhfLqeuGpcdcfSBQ7EtPm3BIsNFJvYrfPL5dxj/pW/R/dXfAgBEk59DfJ06ykl6M0Waxa5VbltveRkiLaMNkMgozhGPMwST9M7rBGh/QtuKt5qVIiq1olFUav6hYmZzRZ3X5n2J/lJRzBsfS8MXtE73bxLaMFj/ue01sb9S3kGiutWYtRu77fMCNjsjVmSC+bVrJQ5j2FocJkNbGSkl3Hej/05c69Shluv1e1p1JCEa5/u593Nv8SGVOYdjz4JSFKsywqgIMW63E7P2Nbwr+bNBoqPu3l17Xi7qYKPLxIk2Yp4l63tPKoofvvsI0+ktgiNdmXcWxAeSn73lQX54J6fyY8WPkhA9z/t3APxN6CL9DwH8mwCeAPhdACcA/gDAX+/7vvE8zwfwXwH4JwFcA/hrfd9/8y7OY9vgtJTMybxto++o4YnZ3b2dCViyrVQUzOvxcaxv/ploIXsPueQoqgD57QjxkYW2bgQYUfiVEw0GkbUPlQeQWQhZ+05VZRh9Y+YgRYgw4yDLa5CmBm4zqDwGiypH3D8Wd7VTbVhUYZrHEKLVKEIuHSQd2EyGSlIsV2PkkiOXBFVnLYaYkRU7sIh5Pc6izGmDbnsxHrXyaSW6ViNzF1cnSPN4Q79UKYq6EShr3wGrrBapMm2yoSTX0KBVAz/Wsm6jqNiYtwJ61ki4TgDnj6/QtByvVlN3/YbAFCuAIMwCbSsTn7UoS4Fccixq37meWPnBgPYGCeoZ2bb7h9WBrZS3tzo8FPa8y1a4FnfQe/CNoIUQLcJxBv/0FunzC3SKbiQdFjSQFdAZ0WulqLuHgPUsF8DO/9FwJ5FYVIGe31v6FO1Q5xH8NAKZVOgb6p6Zg9dAbiI8h9ViU/koqkC3wUX73kjxupW+fjanooHqNKjKbogtDUheU4zrzG263lt0FbD65Vaaedv4hSdEz/M+AvBvAfjNvu9Lz/P+WwC/DeBfBvCf9H3/u57n/RcA/gaA/9z8f9H3/Ree5/02gP8QwF879h79MZk2A6Ao99gZMdKB9XbH3LmFV/MZlSNTc6K5dFnjG8dt7qgJ536zQQO4qgKk7WMUjY8v2Hc7ljB2vpYWkUN4AlqHEdAtntvFRFveHIjF1QnIHylM8C288BrlixPnbOE+99Yi8NBQRv7OEoyJUd8RA7L5viAm8U2FcnJj+2KY9M6iDLMkhc9aqGy848VYKQY0epPS1gJ9M5iryh7NYoQX332EpuWunXkXOlR12pqHrnZnYJ3hEK6yxM2vfN7uJMONz001kOrJEy3MkFsk7aDaymuNurVgppURc7f8REiuDX/3nHtAOnCuXSveNCkCWquU31F0crMZPPhZiSbOB48WYGcZIoOMHiZFGmpivd1YANigAnVqE1izHa+uT93mc9hmBIAqj9B9+xTxKkanCLKb6f0uxFa0tUCarQX1j80m32VMRYNE1M6A+keLn2aIP1rLlAEIPc9rAUQALgH88wD+dfPv/yWAfx86If4r5s8A8HcB/Gee53l9f7jZrXriZKj2RS11AhsS9TdOzvr2DX7dtl+3j9l2FG2j1TCYsdEB4Hb1du7z5eIUlHT44rNvN8AfFmwS+jUeP77SrbYixO1i4pLiXaEUxbV5bTBbbfybFfW2VeCGP9uBOcqhaDsKqnooopAa5F7clEe93awO51Q0AAQAulPhqd7DvArQdh6eRLptNJ3ewi/0rhlFsuPHWCkGWge6Mh3C328J0tenSMsIPtMcPwti2oT27w/rmA5sViqrPEYtuWvFb+tnamf13eNxv8Gj8znKIkSax8i3eICy0y7020R8i07cDuvxyEhn2vkcb5oUbcV5LNwc3CJQjSCAGnRXAtEgnt2CP07hncfwywWK+QyLqxPMzm8gohJ8VEBVQsuzYQRqNgx2VjvkIW7HfDnFV4szfDy6Nee0C5RrKh/N5aODUnJ3RVsL554hzGbnref89wibDBNROcWiHzV+Iub/YqPv++89z/uPAHwHoATwvwD4BwCWfd/bu/kFgI/Mnz8C8Nz8rvQ87xbAKYD58Lie5/0tAH8LAE55jNsqxGm0OeOIxxlErY1FbTvOevJth3VWOAsqnEbZXmCOlemqjlQfsiODyugcjCj87Gd/BsIk0psp5osZbqsI53xXb3ORjlzVKESzwXsCNlGkNimGxpjUzrIAaAqHme1sxz4u1nZEosako6ilQYO2AiFvkJZa99QquRxLjMcioBLP4hyq9/BbT587xRMbw1nLsJLMpdbV7BvmuJlqFenNRJHg09n8zgXSzniGG51VpiH9lnyuOmJALj4CKvF4vNT+eEOBcEPWZoeL5b2xlrILdxwn7huBQZBWar8t1v7X91i2ZG9SZNTKxDVG5ekNFGKM7yMAPP70+3Ubet89OLiPtxVdXt6c4pvbGa4qHyGLEVvBiOF7KeLaqVFQYZTkGJ1oIE1xgCazHdxvEMr91mDb53gsBG8xCUpUimHiVwdlDZnXY+ZXmATlrmeolf17AOf1nQT1gfGvveVBHsb//fMWP0bLdAZd9X0OYAngvwPwL+15qc1S+x7xnQzW9/3vAPgdAPg0OO/TVoCWsZ41mMXLjwvXopGK4nJ54pIigA3yt75htY/cQ8E57zIscKdpBK5ePAEAJM0rdK2P1TdPN6pIpbQPZFQLxJI613sxKiBmKdo02hH93o5tWyT7UE6CArdV9E51SIcRUIkvnnzvXNft55GGu+WMdwebl33+f4RLCNFgFhR7wUm2ZT5Eetp2+H2+Y/s64WuNS8rUQWTuXfFqwH+9b9KpFHFAmLbzHG0iYpbrdvz3A9LjLGgNelpzPwvpGeeL9TUJeYOpqQL3bQbbjoB6WtxcUxT2V97z5RRCtJgoAln7WN1M3IajaThCc+1sBb0t3GCv0XlQO8T2UCTbfp9FFTiFHxHUCGYrdC1DXYQbALFj+sHxJIUIatwuJmgagfnLc0xqgTCN0Em2kVyVDMHNRrKthTPc1h2R9mgyBLQ/5zTMd34+FP1nVB0U/3gvoSrg9o/e2+E9z/sYGg9yAd1/+52+7//T9/aGbxA/Rsv0XwTwdd/3VwDged7fA/DPAph6nsdMlfgMa7jSCwAfA3jheR4DMAFwc9ebWB5QYvheQVzCn6VQpW4fnuEGjCrMVxPMy7WrN2A5WHpRHZLvf+xQiuLqxRPNsVN0p6VaVAHyOtCQcUVx6jdgfoPgsyuQiwj06ho0aFBcnu4F0lgO1IYH4RYcvGj8vfPXNw2fSZzGKU5nS4ig3lUO6chOFb7tJTgMT0iEcem+t2NtL9V7CB+IRra8Mm4Soq2wD1EIDsWrfIRSMoRv0OKzFlBDDiH1sNfGaRg2GU5F47R9U6m5iNto6qHrxSGFGmuse6ySqSXH9UJXipbzaSvvriOOA1rWPmrJ3b1lQWultWUb+Jfa4wLrKt62s4VoEI5TsERvrMLKen4KM0smGC57hCr4cQExKuAxBVVqEfzVYgKlKG4XE5R56NDXw7Bo2W1ZP0YVElEdpNecBSVO43TDf7NpufbZ3EqI1gD5IQpJbxw93nfLVAL4d/u+/wPP80YA/oHnef9r3/f/3/t804fEj5EQvwPwz3ieF0G3TP8FAL8P4H8H8K9CI03/DQD/g3n9/2j+/vfNv/9vx+aHw8iMsK4vGjO7GHilGfUN2w5su0hfDKowFTUSv9rr/yd464j52yjVfcFIh5noMBPAx/GbqcwMQymK19/vEnSLKsB1bkyRjfxZGJU4+fQHeCHQRyG8qASNK434G6Dwhg4F9uEE1tUQGzygdPB3ywOzbgcPEXO2kYgKozhHYKp3m2AsatU6nZdbiXIfP1R/mPslNkaU4zJuR2zAMq6aHFSUPmu1ETLT6iJWMP0hCfFVPsJ3WQzVexsSdPeNtl8nQ+v2Qd0TsZ9GAWjqBfN6cJPsnkS2QvFxFlRIxGbLcDhKsNdJv2ZX9L2TDL3ydto5bUf13HQx3ZDGk91at7U17fhtV3tgTV9Y00D0M1tL7T4v6/V9MRulCOPSmRYDMOoyUidtkxSHwf0GLGicCbiVaaNMYXF1grwMscpjCN5inGROlMLOozd1fNVGEtPnr7nQw9mx3WSGYaXl+u4Q/P+FVonvMSH2fX8JjRdB3/ep53lfQo/E/uImxL7vf8/zvL8LTa2QAP5v6Fbn/wTgdz3P+w/Mz/6O+ZW/A+C/9jzvK+jK8Lfv+14zXw+r96nUKLne2Sai2uBWbS8M22HFgbPGR2narMcsZQA449brIsHo+wv4osF8OcX36RSTPbJedtdr3QVsWPFh66lok1PdrjVA7cIzpGp4jV4I+ka7etukM9QdTcYZKFV4dX3qfk8MPpd1ige4S4bJKFtLj92hUgJAk8qJpRFo/7nrIsHk5hSfP3uxcw2k0ovkEIgjOwLp9Uhbjj+9/Ajxlxkenfy/AIDFP/oC33z3zGnUAtqeaLjo2MS+r/KJwxJj4383nK+ypdLgF94eNHbuFHXyZLaKb2uBphbIy9AhTUszi62Ud9Skd1+0vQfVrZGilGigDe004tT+nN/DCgrQBsoBVXg2unVUhu0Yop87g5QtBqLVluPZl0wb2uaxFqg2mwhG1EYV6ahLRuZuqFRkwyoWDWfGjCqMwwKjOEfdCCyydct5lqTOtzBfTMCiyv15uZy42eQaKCVApQb2WKd763nKkhIR9BphKTo2Ka3FuLVLilJUz5OFpYGsW++MKIR+jUA0kIqCeltJMajNuVCXSFd5DGHuVeu8AsCBiI4hut82+h7of0GTIc/zPgPwVwD83i/mHe8XPwrKtO/7vw3gb2/9+M8A/NN7XlsB+NcedHzoBypmLUK/hvCbDV3Lzihh2IeUkg7TMN+ofoa72X0R+jWCUiJrOdKWIWRqb9USUIVnSYpzg5B7sTjFN/NHAHZ5kDaU1DOReRkjoBKTwYa8lhzzIsFZl2EUFg9u51oNx3aLn2gFwLnfoChDpEWk21D7PhNrMYoKTGa3D64IAZ0UpaFxWLRimk6Q/WmAv/SrX22+1nLwem9jkZe9BwbgZZ6A/8kXLrF/+cc/c/xCLbDMMUvSnc/hqt1BUrTJMByvK/l9s7FDcyjnOTmocptabNA13mVwrzd6uQqMeIAEFDGzsnsmRAD4bLI4aDdlZ2JW7F0qutE1ANa+jXIVwQtXUNVako0P0Klu00kUpBF6sHSeYTK00nx6XshQKaZF+kWF6XilRbfN8RfZCLMkxenZDeLZLTpFUK5GSF+eoVMU1/MT147cDqUIqjx03aONz52UGF3M9Xdoft/qzgJrpURqWpxWPECptVNJ6NeIQ43CloritorcNfFFAz8qtUKUmZtLRaFa7iQih5GWEbqOvF8sAw2A8T/2lgf5+2ee5/3+4Ae/Y/AdLjzPSwD89wD+7b7vN2HxP3J8kEo1PbTkWsibnYXQVkeWBG3DNwoxw4R4F8qLUQXZe7huKAJJcBHuyrSdm3nBbLICZRJl7eO6SPA8TzAVzV5giJIMReM7o2G7uKmOYF7GuCwiZC3HF0Rt+BDeeV1kj96Q6LVVz6B97NcgtEOnCHzRIC0iZHXgrIsY1e9lq9LZ2Y0D7eyLfWjCoeCBNJWSpQ/8SRqhUiOo3sNf/kK7dkulF02rqgIjmG5DdgQgHZ6nE+DLXweAnfmmddKwAu90a2G2EYgGyThDOM5cdeE+S8vAfS0MXzdiA7k6XEybSotVW8/Fsgid9uZwgUt4izMDFDoPDncjOFlvsqTXgxEPqvWgTGOSkrVEIDPXI6C9u8bMmOHKjkD1vaFPdK79aP0Ah9eBUYVmsBG0M9MNs2Kqdp4NK2jQ3QZQpWknbm0o7bPh2qbm+x3OCa1NmxVLsEG9Xj+j/tqw129aJ/3sAAAgAElEQVR0p2Jo09RL6kTDbTI8FEWpLdyCuISQ+l7WJsbr7zcaZ4hSDaYZOtZwv9nQWXWdFquuQzoEBnkdxAWUIgiy1rWDKV0LelOmXLsV0LJ9tWRIRL3DW32voSpg+eXbHmXe9/0/degfPc/j0Mnwv+n7/u+97Zu96/gwE+Jgc9y0GpovawFSaguX1rSwhmEf8OGDHofa3Nai4oD1EN++dlELpK2HlnoIGo6zoHP8Q9sW3F48FnWAhRnEJ0yCej4mLXdoxTSLcZmPsGwoYqbVbkKuHScWtY+rmiGVFCGbujZXLTlyufnwzxczxC8zsKQEb1ZoX42QPr/AajExu9rD17A26NtackRYL9qjJMfk0fzg71nXc0tmTwvtCWdbyqoHCukhoBQBpYCi+Cb38fNVD9n3mIoZJi+e4XyyRFn7mFcBcum5NmC4BZIZJsXhgqrfa42c1Moja2f3YXcgCipMZreIZ7c7yXA77KJFmAQLdLtNRCWYX2vwRi2AGs4izM4jLW/TJuypaDbawJbjadvk9nUj3iCg1FF8mNfjqhaDmeHg3IhunVKvd44g1NMSgplZvA+BkQC4ys9em0NelNv3c1GGoLcdCNWglPx6iuVqjKzxkRwgm9ukaK8BIx3OTJfmRTpxVWGlGNqOYMR127Exbf6m8lEfmb2V+VqnFths/Qu/QVmEbl6e3440NSnZFFogXIL5DSazW8MzHZpga5H+KtffqxUJV4psXB9bOR5Dt9qwY5LbOkBpZqqzoHD2dGUrjqs0vW1oPZL3Fp7nedAjsC/7vv+P3987vXl8kAlxGFJRrLIElHYITKtwuZzsHVRLpRdoOy+xjvVW+9L29O3DvGx8VN3adaBSnkO3tp2HqiN7b2BGOtf+Y0Qr4ny/0nSIqAxxuTzR+pWdh22HRt1qBJYNwWURIWYawWfFn0dbc6Bvv/0YVR4hnqTIb0coy+Coz96xmJ3dYPzsFWjQQGYhmjTa+Pc6j1DlkeOFLbIR5oXeTHBjOqvFpQFUBIAA93rc1B4qpVD2Ci9LH9/czqA6gst8hJelQCE9nIgOAe2QMOk2G/cNB2IYSF/VjXCzsen0ViMT70iGNvSstHMOCADgMYXY0AqUoqgagUA0GM9uIUTrEmKlGAIq8fGp5mtZlLMyMn+WF6uMLFrAWne/tR1FQH0j8L2bqNhAe3Tm15j4FThR4MQHEOg2s9uYeIDdFHSdGxcMKxWbpLcToE0kQ0m8NItRlgGEaLBcjfHt4uze/omMdHgSp5jGmu87L7QgetZyd75tp13sV1ni5PesutNdYSXRGFUIw2qjIitajjSLIYIaLGggZinooEJkUQV/j00UsDbeXpsGMzSNWFvEGcAV82tQFu4F6QHG1cNc02GHw25IhmbZ9wHyvVW83xniPwfgrwP4Q8/z/qH52b/X9/3//F7f9QHxQSZEz1vLTVkuz+J2jLFBdB1ro9SSrxFl4xQsaJCYirKWXLfgzI2atdw5D7QdUHVaNBnQc7Kq07ORYevo0AOseg+vszGSxse8jHZQlfY1siMopCZhX9UMSRntOdpmvJqfQtyOH9Repaa1ZukLZxdXGH38EuKzFbyQg9+mEDchqtczyEq4ZFiWAYoqwKvV9OjDa5MiJ0DWArLv0fYKaQt8k+nkflkGqDoPnFjwiHI0hSEV41DVQ70eIAqBmYFZ8W0AiGqBogowTjLtkXcPqycb1ifPIhNtUgzKVCsPLacI/dq1lSlTGN2OcZVOXDK8+OglAA1I8ZctXmYTt+AFVMI3ptb7SNpniqJS+nsfgrkY6RBAt2QnfoVJsP5M9n7d16IH4Frhw4Ro53vDkIpimSdu4R4mRakoFteneJlN1tf/HjEVNcZhgXGSgdIOs1WBy3yEb7IQz+LK+Qq2HXWztLrlR9WobKiO4Pt0Cur1+Jy/Xo8JTMVmk+XtYrLhf2jbpt4ATTwM6+ZiN1gWdWqrPNURjMzrrIyfz1vdxTDzyGHUjXBdmdZ0mABtKGw3SJViG7PW9xLvkXXR9/3/gf288j838UEmREE6PI5TxH61MYQetj6HYflAi2yErA7AiMI42aVIVJKjUsxBw2XvgRJgZPKrFU22EZjdukWKqUFyHM7SbLSdcJ53nPQ4FUZQWTKUrTCwdIJc6gSctjppBKQ7COopGh9Z4yNtBaaidvynYZWoJAWhDIB2JFCKYpakmCUporCEMInkkMWOrH2XDBfpCNdFgqsqdNJ4hygSldL/JRwAOKqOYir0tbksA7fZ4KTfQU3yAQpRugqK7FS/PpMO5WhdDrR33f25lF3LjNYm3fk5Wu3+7tEOstKow9ko3TtjtUo3++JQ+9T9u/mMnGj7q6kw1JithJDwFmdhsZEMbVhEK0i3YeC0Dzxm+Z/A5oYuLSIsqgiVogh5ozm+xrPxOh/h1sy57BzwEFjHAmq2hdttXBYhrmqCgPr4YpxiEqzbmdsUBivK3hsHFltxAcDLbOJm8WdlhLMDFGapKHJDvB8BELNUH6v09TNhWqLHhPBtlTc0H7fB/QansyXqluNsunRG4QAcwG/7e3DiB4PWsXyfLVMaANO3BdX8X+/kVH6s+CATIiMdYr9yIJl9ABn7d8FbJKNMtzsMb/He7+NQfrs3Kfd6xGxtdmvJu7Jb86zmFUfJqHYkH7RQbeUTMoVSUlxVuk1WKYrnBUXa6tfm0kMkPXDuOSrCsXhZRsglx0cdwdlUS1tZDmJtlDaayodUFL5oEEalU2NhUQUa36+l+CaRcCCBrhiHyY96lmKwaUNkZ4Wq97S/3CBs25FR5dRC0iJCc6nbfFbA4NBiPYyuZcgXE1zPT5AWEWajXceSrmVor2PcXGr08F2Ao41z3VLNiU1FnjU+qiJxM6Rhu40aFaV0q9Mx4i1i1u4kQ5t8ZEewVNRtUGIDmNrX2rScQGC/f6O9r4eAG3sc6vVIRL1xfd1csiMaKV1GRyUP9XGAhEvMggKjsNigIQCaE0pIh7wMIW5HrmpbLSZI8+1hw24MqU12TJKvEo38NOo0QzT2tmjEtuiDTcL75q6ABuU8Pr12nN0/dyEr4Ob9KdX8MsQHmRCJ122oPlCqwMxcx4JiisbX5q6mZWaNQd8EyRXQfocobUWYbdvIOhps7wK1LRIxv9cBSouEWwRqKRmuKh9py/CyYljUur0IAKX0kLYeqEdMgsDBHbeNtOX45vZE01FEu2EY3FQ+0izGdHqLxGhBWrqKZx5gj72/HWpAccCbb/t10s3XbCzqQKMpTfVjqxOf60WzrH3ULceqvN/cCdCJLn196pLhoagXI9y+PtPVoRGzBvZTNvaFvSdi1jpBCM2/ZLjMRzgLC+1Uv3UNALgKOeGtE4neF7bF3HYeSqk3ZUMA0jCG/M/h+W0HJZ0WKTDcO9tmt8nQAr6Gm1GdDOM7kyGg5eimosE0zjCbrJzSTVn7GxQpAFgY9xWlyA5gbl8oRZy7vUVS2yjzcAdEY8MmSI0yNddHUicNdwiZTkyr1ibDN3Wded9xzCnoL0J8kAkR6I1HW+OUJZSkSABkaYJFOkLZir12K4cG3zbsLliRDtJYRVW2tedpaax9My27yLQddW0P2zal0FD5VlJTcerfH85gnhcM82qdDJmnfzdtAep54B5BOEhWx+YrWcuRltFGW7jMQyxX4w2ZtiF3k3B5UAWG+TVGJwp0laBqBPwmAPN63CVKduJrkM2yefhDuK+q2ddOsi3z0K+RlrrdZ6uuu6K6HSFbJSiqwNFyklHmfCABoLyaYXl1gvlihtlYU6oIl9pL0VwvZpxMrJPC+eP7CSBnLXeeeToprr8bS53YrnH3fS6p1ubCa2K/BqycB4dl2ewc+1DFM4xtutG+87DJ8KoKUErdGZkaKo/Vh6VM4fOnP6DtKF7mCX7j0SVmE31dqaH+HOLj2aR4H2UXZTbFizqAz6QT27D/tlxOtEiH+b4BuLEApVqZKNjqAkgDyEs6Ldvmm7m1vleYMxc+FhaVy4meJdu/txXd+a7fS/zFdn/6UBOiBivEBiU6dO/mvp53sGb/jWnbQ00jtG2SZGgqf+Mh40SBYwjsoC4ZTkVjFp/9i8h6cdKL95QrjLhE2jJUg/ew1Q6wOV+yifBt4kmUI/are0HBbajSh0c7eCKDRxt0GUc7QJkSJpGcLKEkM070HVLT6rUtumVDcVVZvVjgIpAGcctxVb9/eapRuLmA1ZLj1fUpmkbgTFFEZ4ud3xGiRRRUboMVxCXYVuWg7hBxAAA/KvH04pVrQd832s60wxWFMsASG5wo1wq9K2TvbUi+tdIDDLm9Utrb08911Z3VAb5Ox3heMDwNPdzWARYrnWyuiwTzKlgDmowWbNH4mJexVt0x1+OQaMRQQJ+TbgP0ZKunX3v2HOe3Y5cMbVCDGD5UiTntT+NQsmyEE0R/lY8wuj5BIBos0hEuB+Lq2+9hOafL5QRRWDpkq1TUgNMOpyeft/DR6g4MkwMZOf3ZqjxCU/lOuzfNYodCBnRSpG2PxK8cqK3tKFQd3KuyfuOgAbzpb7zlQf7Pd3IqP1Z8kAnR8/RCNhTtJVzCox3YosF0lRxtjVrItAVhDMV7h1WbFZnmXo9ZIHEeVE6G7bYOdmY8gE5uqbGS4V6PJ1GJEW+QtALL/5+9N/m1JEvzhH52BrNj0x3e4P58iMiIrKTobkotIXWLLWKJWLfEihaL3iBBS/wBLFix6AUSqxaNEBJCbNjDDoQEQgUFWdVdWZlZGZERHuHDG+5ks51jxuIM1+wOb/AhIutVfJLL3d+7g127Zuc73/f9hsbX7ayhTFm/JbMP430So6AakDEVJXzePtg7TlU+yErACxuoXDgQA6EdWFTBYwqpZEhXE3Cz2LSdh7bTyf6yJqiUNqS9CDs8iwrHkwN83DQ/TFIcohRlR3G50jqbF8AoKRKjQGKBRTxoDqqa3CcIk641/9Cwqj67lZoj2N+FtDRJcwj4AoCNJIDZtADb6zmTDN8W+tqtlIfLSiBm+vu8rEIsG25eKzLPk1jUOvkwo5xzV3DSO2upgGl7Mzt3JYYge8yX5Zg9kqM7dBSV4fNlrT5WSjzN4d1MIViLt3mKrOVHj9VKsdWNj6IMH2zF5PPWEPk7vf5AXwNUUS37ZjbamyzGIkv3NlSzMB+Jheh55ydGmcoK/U8zxMcXHnp3MQ6h8Z4vQSsfftA4yx9KO7BAew1acEDbGU6QUbm3Is/A9mbkRCvhaKWPEOeiHCHh7OO52UHbqBRDLgkuKw8/T/TiETBpOGbKtankDgJPHcHLnAtg5nf30q6072U/+32DcOk2Fary4bUMqhpog0YVaKgTBQt0C8yp6/QeNpJg024rw+dhh7mv5bn0HEtB0N45t1MLrCEe2geMWo4tbsPPyqjSbV3eAq3uCKiO4PXyBIwqPKFqZLJMmQKVWonEVnZDF/XwfIGJscyazFcQ033QzTAs8GP/2DUq81jLvu0oAtOEHiYEWyVqvuHxk2X5q4BGP1Nzrtvew8IlOH3eN1Jza1Wvf19KiqtKz+WywSav7TyH4Ny0+roGqOmSUAQ7VRQjCi/Spbs25qLA2WTlZNdsdHLL8dvO6cZL1fA7tc4ZFrBTtj6WTYCs5c7vUXVApSheZSlS3h7crAJjWT67HlhrKTuPPhbuexn8e3itDEcQShGss8RVhnaNsInROrBYndjhPfUpo7+HafRjjkeZEO8bhHTwRe0kn+g7jebLmgSCtfDL1hmPFk2wlTBjrVu4KOn2JJYAfQHH2GpBDm/gdrA43RW3tcPOBPBlYoTGW2bAErqtMhsofWxarqswe8N2GmDk1z64ESuwNARm5iOdZCBG7JhPcni0Q2+AA/Zva5rabCLAtE/LdYpFlhphAYJceijk9vM+DzucCb1QVkpzrCpFt/NUD45GUkqdrNreM4utPsbS6FsKqjcSy8YfCRJYMvPugjw6rwbdaUngACDfXUAqinNDxC5zLe0Vhrejaycv347c3+8b+SrF2qAhDzlv2MrQXgO75rk27looW4NsbjvPAL4MgAtb2kbWMixbPWfcDetHubVH6wyAyxt1ACqzmGYtd7J/h1rJF8nKUVPSC616JItbOjbGz7DZsSbb/bcdd1SKOVqUMh0W7vUuOdoqS/Yelo2PkAkI1jpVoWFStEhUAKCqg086VDuUnabh97Jn6hSBkno+WVRihJgdboSG58yKh/8wZsEe8FNC/NsbjCqtgZgW6Fo9JKeG9mAVVhhRWJaxe7wwjt1OwJfohNR1ZNSGtbt1YR5rH3/b4iWNso3siEtee48xoJp5APw8aZ2l1Osi1q0sSbCRFFdV4BY9QPPTrBMGWIum5SgMGk8pOpK5AuCqGBZVYJMCoB360h9VhgBGQuFt7ePd5Rne5ikWtY9KbZMh9YDnocKFSS7a0slomRqgB6ARuymXrn2neo5Weqg6D1VDUXUC3LQKh9zL2ABOrNqLXSC7I+dSdWSUDAGjPPTumZ4x2x0+6bSPnqTgt+hzh+cLqPL+At75KsX1YuZcI4abLGCbhDaSIOH7BPndOHZd7SYkQXskg/NrE6I1DL6sxwuvrRKHbVVrV7Vp+agKs1EZhKz1KHTHSDrMJ2tM5iukF1cQX1zC+7l2Vul+9R3y3744+vnWWbJ1nPjIAtevCytyoPD09Ppez6lMcnb39U7FaqNrGWhYG1H9AIurk5FLh31f5zRi/lIdGclE/iDRA/gJZfr4gtDOif3eFowqsKCBFzagvnQeiQBcUhzC0q0QspUCs5Bzu6OMjErLiDhMlLNuAfTAPGUdNoyahWkgE0XGu/bdGCJMzwNNzTiNMnO8WjBgWTGzawc4oa6dyggdtRStQgewvYHtQqOMALgflbrlHEp4tEeHBhR6lmjnh61JiNbx/PvVfDTn4ATg0ACac1HjzLSVK8XMfMdqigIp061Uu+DK3oOgHSq1TQjLZiuVFzGKl1GDz+IMiZndWkWPSjGER6rrriNYljHelCGY1yMZVJeVovjrxRlmfu0czYXfHAQg2bk0javRhkGVgaue995bUVR5iMVqgvVOu2wYWvSaYtN6mPHji6JV49l9naHvoBUJn/kUc78dicpbj0FBt/zYvaTY6WrQ6qPaOfk0qNysvFIU3NOfOeHtXvs24C1mkzUmJyvEF1cQP7tG/3e/QPVH/wCerBGwP0Xif4viN8+AEfePOdCJBfDAJKKPmRhfD2g1T0+v7wU4a4zlmhisA02zj7gFDJ91leLN4gRl67vrle2MVPa6BHdQXz5qMAHMP5SYfz8E9R9qPMqEyIMG4fk+YnA3ikqgXCcQ6wi9sYIZJjPrw2f5WmWr9S+p0kRe286wMWyt5bU4CFdP/ApfphkEjZxHImCSrbflLw6pG+oB933bedi0WgFG/58YHVCy1369bfepBbqP34TWRqpT1CXD6zxF21HNieMtlo2PqypApTwtPnCIEmCOae5ruspdZrkXQg4AGQqfpSsErHULifWcG4JQdj9nXgssmwCLhjv3jIS3Dh28/cNwFmWo7tE23T03u+du6P6RbfTsSHPgDpsU28+QS+BNxXRbL5ZOf3T4mLYzkHzj7LEbiV/jGXAn4OXMyLC1vYdlQ6CUvp4E6Y9WqNOgQsxadxyCSu3uMllrDdcs3us+3CeU1BZaZSmc84p1wBgS/nedNxaVdoLJdmaOG0lACZw+qm0hD2NRBxA0ccm7Mbxl1REnsN0N2tf252JgkG3tn4pKIKy0DiopJcp1grdXpw6Jq3rvoPC5BQUNK+sfrEqUFfrrv/ph3usPNB5lQvS4AhXNXntvN36/OHO7zE5RfPv9M9cqPRbZYNGZxPsVqG/MQI/5KVLSYRpUbmc+AnzcE6H3Y4aVs7KuIYdiLgpEfo0zySE2U1xVwuhRHs/sCZNOp3QY1jKKesBnUY0zUWl+1gCSDujqtusIQt7suX4MozHzrWGr1JrRAthLOA+NrmVosgidoh+NfP1V1qFSAUpJ8WWajaqv4abLVsbzA7JtdvHdtIe/M6sVOg9q08Lmd4o86NetXLuPkA6TJMPkRIOLaFhjukhx+erZ3coxcjzvtRutdR4jaw4nVDuKsOjSVRXizQ4p387rlw207CH3nPpRIT1EbCuRKHsPm9bHtUFIWw9Dqege4MkmLmsYbSkj2lpOn+PrxQxKEYRFiNViilUVoZTM8JcZqFGcsjZco892T2H0jx4/zRAfYQgK/49rdDcZ5NXhBCeNnNVX109cr94KLG8fs/UuG4bVKfTNzTBGpnWIwnI0jB+GnUNa0V73PMvJ+oQJ8S54/p2hCHpFIAsBaQxvlRxXyS8mC8wna4RRiab2wYiCoNMROnEYgqqRIexQ1X9YJX8W1XgZZ3g6WSISFcKwcsRo27KtGx9Bq1/L2gcNw855LehCdQB21nxmWnLvE13L0K5jbG5moEyOOIcfmhxfl50RS0/weZIfnRleVZr+8DLZ7CGcp6JE4tdaFu6I8Dr1eselvQ38lfjVyABXxAV40EDM19ox4qSEFxH410uN9n51AUq7W89D3xCnG1vmuutgE9JdqOhacvx6NUXVEcz9bXJVRvN30XRgnufQzrIDqq6DIARIKE595ToEq1qAE7WnRyo7CrrTrtEz5tbxnbmk8OsGlTERWK4nA8NtthXl6Mj2OzDXasDknQ4hH3wP3xY9fkqIP/YB/FjBiHJV2verY4ynnefQMUCgafmdJF0L7Bii0IaVY+JXrh0TGsi4hbiPhb/Hr3uIm2hnh/q5ACN6hhezDmeiwllYjG44RhUiUTk7n93oJEPXMvQNAfwOKhdo1zGaYrsLt7QE+zrWhHd4ngSVTnHFnYN7gERCJnEuAE58nIkKszB3yTCebsCC2ulN2vcMeIupKBFIjsivtx2AHYDCXSLJu+1lZRCCgN4IMBgnBEj0DYPnS8gs1Lqnixl83iJN8pGayV1JUY6qPW2KXEqgUh3WXYN3LaC5f/EoKdrq7k0Z4qrijlLxMtm4hBgwzWnblJF7nh0FuLajeR1OOiS8dRZm96H02Ohafc0Q5QGyB+ChUwRhXGrpsqSAf7pGrzx4TQ1P1vCaAl7TAIqgM6OLdZa4GesusnvvvCmKr9YzfJ0zLRgf+ngWDrwLPT13Z57n7ourpkXRt+AeBSsCUI9ixpUzrr4qY3eeLJr5vpJ/NpRpt+a1QCX5toVvdIeHTfjKqFglfo3zdOXartbH0V4bsyP+kh8lmABOP3SG+OajHMqPFY83IbYSfUM0mZxL4ECBMhQjBg6nNe2qoEZtjeHMqqi0lJPvH06KtsVqF+NVFTnXgySokEaFW5CCVjuAW8AJACwbjpuGuJkgoHe2y4Zg2fi4Ni3eRR0YasKWs8iJRhWei3ovGVpBY99vQE1CtEnDpq7CCB33hgvWbCJXGdqw5HXfbxApaoBGZp5HO9fWYl6PRR04/pd1DLHo12Nh26hzUSAM6lEyZKIB0GjaiGnfEiPsvkt1IRaMYfh6xyyj7Gyp7RgY8cFJ6GZHdmY83NxYz0xCFTY3M7y90qjJoQTebWEVaIbx1XqG325C/HrdI5MtNn2NK3qNp+pcGyWTfpQMLbDGfibry5k1gasSJ3EO33DaKslBTVViNV8tf88eNTMmw6o5XNkPux90wK2UlQ8sdMuRxhWa64mh8CgEcaEpPKH+Tvtfv4Eo/lcAgPpNic1vX2D17gwLY5VlP5+93+4LLFH9eEOjNVGNjZi51GTH8brZT3CblhnhjMDp6ia8xUVYAEOxcsNfLesAk8G8WGsi+yjr4KAo+rGwyHJOFObTNSiTKN4I1w7OanGwtfpRQ9bor36aIT7a6Esf9SJFMN+ACr0YEG4crAcoO0e27z2gO6wZaBcWu9O2z2la7py7jyVFa90EaAsgQItPp2HhVFC0uShHZOgC2WqOTDJc1nSUDGXf411bgXkhZj7DzNfzlWXD91pcgmppuDNRIvErdxMDesEevnektAuEVGOboyoP0db+QaFjOzOxVaIlEQ+DmA1FyKRLgpe176oOQcnRWZX1gZsHlRNit8nQTwsHZ+eBBjUUg/nRoRbbLvWlPeD3B8Bx1xa1D+b1I5NWRhVq47qQNQGmNyXSUG9qNkWk50r3cNEAjM3TAGXcdpo4/stFhN9uJBZdhdKrkdN9KzJgmwwZVU4UXh+/npdlLcdcAHFQjTQ5yzpwgB4rwu2I4b0HpTQdhvUeZn7rRMF3QxoBi6bRYhfu5yYp0krff4C+VoL5BmyegaQeetmjL4Hu1QZ9yVwyvF7M3AzPJsOhzuhtXqb6HNq/t8dLva1uLvesKAFB1XEs5OHvatnqCp2THqLRCG3LKbb30FUZI2Yt5s1WjEFJzfG9LpKD/ORjkUmGhLeYxRnSkyUIk5gXIRbriaN9TUVx743We8cPgWb9A45HmxB72aPdRKjzyLmbM38M2nio8oOlXNg/UlEQ0zq1cSgpqt5D1QQOsTo6Btq5ORNVugoJmDYSvTRakynXyS2XHi7rHgodZN9j0xL3GE1N0K8pqEmGfo+5UY5xx28Su0sOA11NqejeBaEkQ2lapGFUQsQlbovdeav7ufncb0ofG0m0Mo0h5d82N3XyZMNqb0d0nBlZtcBvwAygaVdZ6L5IPSuVZ+eLIdPUGKkorvMUjCqsaoFvshi5JDgTCS5CPZvb5ajeN+xm6ev1FP9qJfDn6xpvvRtUNEeLGhwBRB/jhAmcBL07X/a8WLunkDdIuRbPBvQimzUBJmExUtqhZpNi/01IBwbd+bDJH2YhrwCw3sPesBVbK6eq8RFKPfuzusGy8iErX7fddwFGnMGDRNd4aN7NUF7OsbmZjZKhBevYlre934b3HaMKYVihavTGxVZ/bXd4o2OTod2MHZI/tM/Tmwo9f+TEg8gjcNK5c31VJLisBDaU4zSPcWY+u5IMZR048NJUlKYrsT1/jHQOQKftywJkLYMS+r21M0YKqmcAACAASURBVIamatWSY9kEn7ZVaqLvf1KqebQJEU0PVQbI1wkWVydILjOIuESVh/j2+2e4LpI7SfKAHn6L98BYWLh2LvnIxBbQN4RdBK2Dt5WosglFUImUSyf4rTog5XZhipAw4qTOhmEXBU6A80AiPcAJ27qj7+9e12U0qhKtKofwdSVpK0JA74bb2ke22QKXbFvRh1bw6DrivPU2LdOuHp3hJx6496xSzkOCcIkgLhDa1uk9zH+tlNmhWSygeZHzoMGLVNtg2V26NIIClQGdADCuBHoBtzPUxXqCTREhjbZdAFtRV43vfDdV77mbUPaenoH1LVrSoIVeBCk4XvZnuBAEMeu2fFWTDC3aORIVJmGBaZ7i6/XUyP/tX7zKfCfBAT8SzUmUJjEYRZeOuEeWkkFQBu5vr6muIyhLYeQS1UhM/9ZQRPNZFXVu81LR0SjBXn9Wnq1uudvQWWBV3PCDCOWHxjCJnvoKM65lB+11MtR0vaoErioOQRm+W88xeaNb0leLOa6LZCSTSI011vA+tKMYdNvK3nZKNEVHOtF4K9gxpH18sviJmP84oyspmizC2+tT3b5YaI+2rBGo77ngSqN4IuhhKbBD2oUAnPZp2fpuZuYU/knnbvzhxW3bjsrsgq3G5xAxp28MBoC4WZJ7PoGzbkl5j5RpYMQIyWpuvLsIzUUToJZ8zx1iN9rax5VxDxiGUhRNo4WRdXuROU7nfSXrHhKES5CWQcTloNoet36HVJi7AD2qA85Eiy8nSzx/oonG4arGMk/2kIeCKiTBGHHpixpKUSw2qTYmbrUxse9rXVzbslS9d2i0PQqOAE/VOc4CrqtD0hmXCA00mcQ5pkYH1G6qTudLJO8qfL06Of4ZD5yDIcevbH0jGsEgqNL3gvnTdvraDnkzOrdlHoJQ7QRz76S4E3aUkCY5fFGDrhPkZeiSIWAUpvwGIi7cRkhQeXCDeFe0vYLsMOiuaMELO9u+rAQWZo66aDgSA3i7rH0nVfcqj5Fcn0OwFivze0G1c4id1Q/nn8x0PQImQVUP5nF3n3YdcfNYpbZ2cW13GLX+McNjAt7Zv/6Br/LtRzmWHysebULsSx+bmxm+Xp3gdRGCE61IkhqI/10he8+ReO9aQG1QqgyqMHLVoX2N3bagRXhaaP5Q8NnnLWZhbt53LBQQM4oN3ep+7kbEbDLU1WHi14j8Gj5vxwn4jrZeZpBxx+YWNhlebqajKsWGtcqpJXcqOoJ2mAEQxHNISNs+ArZoyd2w6NK7wtrr+EGDsgj3ADDAFq0nqIIgPVJ2GGCTMInIrx1KNB5Uuza4cepgRDm1ElslWQDLr27OkfIWL9IlwqBGZ2yJLPmfev1WtNvrkXIg9QJcAaNW6TwAZrzDPGjcRse6RNjZIA+24uHP7fcgOdI4H7UsZUd112JwbizoyIat1KlrL/YOmeusn+h2Fi8NmITmGhnKAfhJga7tjgs80A7+fOMUjyaND5+3ODu9ca15329RNz42O2AuSnULmAnTLmcS54G+flJ299xOA848TIiPkGnBc0H1JnIeVEj8Ws/+fX3sNileVoGhBW09TStF8NvVDOeGIzsMN5ow5slWLm+E9iad29xKRd3GZjpfGc1T5gwA2ubTkfR7WaN79+tP9vp/E+JxJsRKYv27F/j1q8/wKo+wkRo6vmwoYuZjHjSY+Q1A9hXklRH8zVqGjSQ4D+S9CMqUKsSTDOnFFYJNhE0WIy5jw7FThpi+BUF8dnqJ+dnNwddiVCGNCrd4L45YVal+MPMwawD1tlyyaVC5ZOX7DZrGv5dqSNn6msfXe1qP0tzUSm13r9k6cZWOReFa6gWwbXG1dvE153DmW2FvDdQYJiMLXBmG5TUqs+DepqDTKTpCnNooKoF1GY2SmZYyMwa1ByrmN6XAPJtgcqWrrMUmxXWRaASwZMil5rTFLMDcIH0DrsXgA7/BYj3Br27O8dtNiJcRRcxiBMbHz3piAuNKTR9ThwvBUZTneEsv4fc+BPEw83uciVpft7eE/X4okzibLVE3PsJoO/cd0X8GbThbPR9DRtpkaCkDynQ6dhGy+r21Y0jwdAGVC8gsdFJ2femjLyug6QFQkGmFaPpaV/kmmR8CcNnjstQLey1YgQhOFF7GBSp1XACg7T1nxg3o2bzsGGY+XCu6UhQLozKlzKZ4MUDaVpa/Cj2rpLR399swGareQ2buW0b0LDCXWkHntu/Q3WtSz11fvPwePm/xenm82v9o0QPofwLVPLpoixB/9at/Db9anOx5wA1dE87FGCDSdhSbluN1efcMahiMKoRRqZPh82uwdYnTtW6X5ZIjZq2TnYqDCpGoXJvrNrPYebJFr+0mxV1eog1u4PIzv3bJ0CIMfZModtX67xNSUd2GNJJa91H33w3LhQSAqzJy1TNgEoNpq9rE9WKywPOLt4gmGWqj9rFeTDE3lYG1nLIyclUeojFWTIBe/KvGx9v1bMwhNe3oQ8lwqOfZLuZbdLDR65S9h9elj5tat39/s2EApphX4Wjjs6gF/nIVjdradwUjHVKm8DzykEmBUmrqgaAezoP2zmQ4DMo0Fcj32xGgZhi7bbhDyVCZNqm1JUv57d0VyiT8qETw/Br0tAcpNiBcOjPpdhPBMxxBEirgZ2cAgJC+Aw1r5N+f3+kZKc33mhtaULZJDN9Vb3I2ko5mw6o3qFPSa+So42FqkfyUaXeVxCChM9Pi1+Lqh49l2PoPmcJZWECwFpXkbiSjeg+rKgT1euSSO7DTMLaAt4Fa0uh8Kjz//DtEYYm316dHVXs+VvwEqnmEUVUB/vTtc2dls0sstklRa2dWCMlW0Pt1KbBpPaT8/gtZ4DdITpYIni5ALyhIuEH4eqPnSqVOhpFfIxIVorB0JOXbwqLp0rBwIIjb3LIpAWi/JeEPSeza5b02oA52J+KSUQXeewgOzIGa2gel3chu5z7xLCpwFuaYmITIicKiihxhXyMbty3Ti2SFp2fXSE6WCJ8s4K9jZ6ha5SH8QdVjW25lMZbtkoriq+sneJXHeBnnTr7Mfr6Qac/JY5Jyy4bgz26mB34+Xmx/s2E4aajzGeRerxOqAQ/dN6xSUcwovkwIkE2QSYXnUY+Xce4k2ew5OgSMUoMF/C4DaNV7Tp/3UFiEb6Uoli2F6rfGvo7cT7oBSEtBxFpUn0w79Oen8IoSFLqdbpOiXCRg8wzeswnaz/4YnmzAmgZcrREatZ9OkT3O6zC6jjiajb2eBZVa2q/l2LTMAdKoB+DIxiTl1g5LOWBOJtno+cOwPp3o9KY0Zbo6tKAqjSjVnE67udsoelSpafd827Dm5iwxreOkAKWd47l+quh/AtU8vnAOCl4PSrZfMPd6B6mvjEwTAERmoXzpTDpjt8NsO23HdFsSycsQyTpFcD1BEN6gW4Uo1ynKW27o3SBm/gQATYOjFdhQOYp6QGJ27LIjEMTDuajvxX+qB1WiUgRV47vBPycKwngWWlEBK2Y+DGYQibbVOWyX2r9te/A2HVMbdqE/izLtOMCkU8vpWu2Jtyki3Z6dZAiwFdJuB5WhnY9erad4U4Z4UzEwEuICW01Pm+yptzVyBjT/jhLqDHJt8tMgJnucturQ/y87TXsR1NMzwMEMq5C6usolR91y7UVpZqq71Sk1zhvaOaJHyjxUHcO/ebLGRbJymqG2vZnGubtmgHEy3P7M3uL6+1ZKu8kPv5NK8hENYzdmfuMqxGGVyqhyHQ/fbxBPMkRnRlS/6eEVJZCX6Eo6cgEhXILND3Mr7woLUrHnwbrZW0qUoGrrhdh64FRvFAF9/ydcam5pETg/x90oJXU8ROsfuT0XrWkf6wQXM13ZHTpvlWLGu5OOlJGGiPPddcXn7SgZWmNzH8C8fQd1i+D+BwcTIB8MqvndRzmUHyseZUIcxrY9MtghEguX19BoCwipGh9fEC1C/bqIcFnrXeKy8cFI59qewxtSGtTg19+8xNk6wfzNDao8xG+/+RyLKroXgGcYlEnQwUXftHxEW6g6Dc0X1NzggypAGhL7fdQsmpajaTlIqUWxrUwWoPloaWRam+spiiY46Ps25DPWkoO1W+cBy1HLJb9TJm0YZ1GGi5NrR/Ruax/MyMW9T5v2trAoPxuCyqMKNvcJToCZ30GQDpQAl5X+Hr/OGYDYzVK3/FKzmTHIYk40L23mN24RnfkNnsYbpFHhhA+GQhAWSGTjWFK0wg+LTerEIY4FHdCC7DE9i8rRQm5RrkMpPT8q4ZnWbPs2BVMZ+pKhuZ6gXqQj/uj7hE16x4IbFaKQGUSsrRA7z4HqtPWVMhSX4OjoAbAbHrtWaITvPKgHmAKOkL2fk33b0dG1Z48/8Bsw0WyTYbgVFAlOVzj5lAmxrdG9+82ne/2/AfHoE+JW4sr8jR5QmkPGDNHWWvtQqtGCYVBjupkiyVK8ygUqpZOioJFOiGqsVLMsYz1EbwSuljNkjbhz0TkUdmHz0YxmYdLMM3JJTMWhE2JsqBW7n/chmotlHYzmSJOwQBrnSCaZ+3zrMsJ1nuI8XWH3U1lkaDV4DdvizZpAq/vfkzt1FmU4ny4xP7+BH5WubdYUIapci3c/pE17W9hNw3DzULZ64/MQ7U4bVgghZcpVearbyu59nTOoPjq6eMpBVyNk0gmeJ0E1oiEAgF8FUIrozRPTvD8rjwYcToplKbDOY1wXiQPy3LaQc6JGx2SJ+va7DJhEGup7Jz1Zao3SuZ4Xdi3TnplvJ1BlgGoxQbFOIOJy1Op+SFgT3aFK1LC6slw/i8Zk3lbAgBG9sbAel/b+KCXF1zk7ykUFtklRUC1yMQ2q0ftaCsxDYysAMEjcXq+Fwie5S4Yk1K/doYHXMgTzzdHX/Bjx0wzxb1FYH7SHRmtABTopCsTG3LNpOVZVhGUTuBmB7GcOXHEf54oh0X0IJqB0ayps3dPbXtvVJFyjDsNbADl3hVQURRNgUUUO/Wp5ZbuRNVp5gw/oBYBu71jB6EpylxRryVxVu1sdvo+pa1v795p9AtsKlhpKQMIkYvYw/taxBTLlWg1l2egZEshxcJOgCi9jhZj5eFMxbFrgdxnFSdBjxju8jIuj6iNWdH4W5nh6eo3wDnUg67V4DIyyyWJsigjLMnao34Pva3iIt/HdLIVIGEWe2+aU9fUUTREiX6XYZDFSc5xiul3UPbm95vqGaLQpl+iUroLj6ebOWeiHRNuNxxBWUB4IoHpt0Fy1wOzhOLS9kB0ZtclHnp1/AJJpPX6aIT7KhNgZbhDwcHm2Q2ErhqzleNXpHXZSahudyyo8eDHLjkAdWPzVgGcEWLWXw+9L6b4ruNVlTFl3Z3tPc8M4aB6CG1UZa7j6JpvqWRXp7iSHA3ouu6giBJuttqTwGyRpBrFpcLmajarEu2JYxVoEJ5Bobt1Ng7D2x+dJaXpAJTnWeYxknYAFDTpF9Lx26MBBtyLsIZPOBLhSFEIdthLKmgC/Xk3xpjp8S+iKXMvNqZ7gpr7fwnEuaoRM4VXhY9Nqs92Z3+JlqlHG2RFKzVSUeP7kEvF0M/KdtOdEGXRlWYQIo3KPqqDRwAxlKVBUAlkjjvJphzq9kajAWuVa6JViowSqN3ryvSTqACMGH9Yof/8U+D0Q/uzPAEVQXk/AUx9sUriZsXvPuMQM2ljZzaU/cvt8GMOk2Hbs1rbqXaE9FpkT3heUgJkuwB9aeEyAnP/xB77K32we46NMiMNQvQdBNceImdYPNaaz7xOVovg2TyCOJMJD729DKupajnZhs27zT86v9p5r3SL29E8N1/A+3mh1o62R7Axpncf4bjO7F7cS2FZ0stNi0csyRhjUYFRhfnaDcLIVjb5PUhwuosNWkzSzWpGnbkbr7yzymujPUDQBsk0CHjQadr9O9uaL9twJKl0lnbVcK8tQa3Nkft4E+OVijmUzPietQRIKCpz4HeZ+azYheq67vIUFkfIW06ByUHzmTfF1HiBmHV7GujUMALHhSA4VcBK/xovzdzj9/Hv48w2UEcku11o2TCmCvAzRtJojWjf+ntUUoKXzikqgaII7E4hNhk6gvbQVzH4yTILKoUofEpRJrdNpZqeqDFD+/qmjz5BpBY/24E0BVQaOlwjAqRBl62QECPtUETKJeeCNqriHRmuAe9eNrjSpB9fheV9JyE8ZfVuje/vbH/swftR4lAmxw7bttTsPsrY2Ye9hZhRcbEvGimvfFaWkWDQcKbu7LXqoQlWSgTKJbJPgcjVzck+n8+W92kPc68FJb+Yk+vHHDF8BTWKmVGGTx/huPcc3WQxOegMwuN/2Vw5mHhZmz4MGhGsjXN/Xklsoo63iTBOMdDCPzTaHPDdBFcJcE91jVY5J5Y7oT7VX3CARUrNAH9JnHYZtbVu0adYE+MvlDH+5JJB9j3Ph7S1UFl1qVUY0j7GHoNvHzvwe54EcCDFIJ/ZtP5cgus3NBko2VeMjYK2mP3g9KJV6rh2XoGENGuv2PBMNeN2gqVI0xny2MHNqWmsazBxwSbGpfeRliNyQzN8nHBp2sPnarQrtDJMc4ScSqhCbFmk0yUYzsGqV4s13Fwj8Bi/+7l+Pn2dez85FtXD21oy668jB9jsnyn23NhjpkB4YB3ye5ABiPItKjRQ17irueZ5Wr0k5gTCtYgvcySW/tUPjZO06AkE4ih0h8ZQ37vvmRIGRDqfxp50P3id+miE+wugNtYLeUgSdG65eHG71L42WsuMAHqoArWmq6rTFjqs8d8IunMDxRGBFnjetJo8T0mGS3A+ObiXPhnM/mxStMWk3SEYAUJvWZNURLFsNOT8XcDcmsC/uvcwTXFahWxRfzq8RhSWKMsTy8gRhEaI0hq4AkIaFUz4RrEW1mRq3hONhxYvtv9tOI3ettVbgN8jLEFkjnKsEId1o83LMeutQKKNRy4lCpbQiUaY6yK6DaNleQmw7oOp064uT3rW/7ExR0B7nwWHFm4eGVfwhVLmk0CvtJK8kdeCYVbVFBbcdRdwRNA2HMEItyigF3ddD0KKEgS2yGcBow1cpCi7ZqLInVIskeEyNkmI/aHcTqpCeLCHm69Fjqlw7yUtFUS0mEF9cjo5pzyXDxLFqlxEF1RGXFJ1XpAkLfLPfOwB8mY7vt2FS3LpS6I1MavjEAEAr21Km2LQ+kiYYeTYGZjMU8gbzoMJlFbrNxbMoR8CkO49tpyX8IlGhaTjadezs6najPyIU8LHipxni37JgRIv3WsNZOoCtWyCL49H1HqpO624+dBY5NBa+K6TRPV2aymg3KQ6RaO5zGBRdtLMbti0e2nVAq+XE7BI0dmT3cN1rB/hdbcWiCVz18e1mikXtI+USL9MVnj1/g3CSYfH6CTZZ7Hhgw+O2iao2Cf9NvnXDeEiojjhx7Hwwaxs6p9uqcFhZP9Qt466wNkCCEFCy7T7oeaBWvAmZdC2y9w1r1hv4zZ6yTKcI8nWylwzfN3YrPjUQz65brpWJvH7PG7RSDGi23F3bJThWIQK6uvPTwlEy7hN2AwDgThGLYTifU9Jp4A/Z6q1e5ylqyVBDa7S+mCwQCe0Duqqirei/kW0DzH1mwFkBk06eUFCpXWtahmXjg5MYZ6Gmb03Crf5vCmCmKJ4YAF4tmeMJ2+s4QDvaNFerFDSs4fsSvergDXiQw9nqR48eP/kh/tgH8CmCkR4x60b+g1qVpkbMWiTGUV34jW5NOaNbCTRbW55SUgeEGNrBObWKQ+9tBHwfGm1HsKgid2PsJkXt8TYW9BZUugXNetlZNGYFQDCAKu11t02KZPSa1w1FyPy91q9Fli4bH1VHMCedhtlPMgSnK6S1j00W42o9xSzOIPzGmQ0P25ZTUbiFZpjIAtaCegKCKi1yfUurZqhDagEgbLiJMfSDYSjJEIkKSVkjqcUI7Trc3AgqMeMK54EGj6SDESgnY4ku6+MI6GvAJsN5UCFgErVk4ITtzWcD1uKLqSasz4MKn52OKyEAjuPqHEkG30fXMtR5hHWWvHcyZFQhNOpAnHQHZ8i1NMIBkh8F4OjnMVcJ7bpaeL4EWot6JWAmYT4kLKimU/Sg5Jzwmz35QZuAhgL2uy3VJKiwqkJUiuFFusTZfIEwLhHlIdhaA4ksXWp3M8BsgjXjgpA3YFXoNquLOgD1esyNGP7Q8gsAmipAlFXORHroS0pIN3KMyVepVpYSDVho1gFFXKfgkwUXIE9/8YEv8hcf5VB+rHiUCTFiLT5PcqcdyLweZ6Jyah/2AkzS7OjuU/PBFFJuUH295+yVrKSVTZpD+sOHELtV7+GqSNxiI/wGTavdInTlp/UYD/kIHnw90zI7VKVu1VY8ZC1D5dPB7+5ppmuQp3TnhrZBqYJvHDco6ZzSCqAXaOvosVv9WpcMAE4Ca8snHVeHt4XvNzibrNB2FFdltAd7B/R3+WWaQfUpvi2o+fzb39v2KfUsyMbMponmph0SHTjUag9Yi3/jyWvMJmv3M8czNXNRmww3eQz/pgULGsfjW1xpZ5FjYdvcFpFqLchsnKcrTZlZdbgqErQYn0sbVotzF106jPaACLhHO50MjRm3rQZl7YMBtybFy80U85sZwlcnoKJB9W6O9eXJyH+TUOXUiIbz12HEoZ43D68L28p3n++WWftDYzgW0f6anr7OzUZ7GM7vsdMqQ8PNofAbzM9u0CmK66sT1I0PERfwswLsbLsxtqpMnyzaGur1X9/9uEccjzIhiqDG33n6PcTVE7wuYpyJyrkmAMM22/EFlVFl2iX6gm8Hi3JoLIO0ADBB1jInofYxwiYZC5zYtPxOVOzHIqzbsALYuoJ7/5uQmbbu7iyLmAQ5dQLrwvEW84F57p3HeQcIKfAbnKcrcKJwZUx+h61sq2uqZ0kJvi/tBsi8/pFDaDtdMSbKemVK83ONlg2ZRDL4zM8v3mL2/B26lmHx+smeBJdUFGDbDcP1YoayFAjDCovV5NZkCOjNT1EJlyyKSrhz/mx2g9OzG201RpXmjJaHK82sCR7c9nWtUNrB8ztAWUBMh07qpNgpAj8tgAP3yVUZQf7+Cy02sKfNylwLvGk4fL/VyaL2gU2CqvG1O8wAZTs0sKa0wyaLPylNYxgBax0NRhnzYxuWNsSJgjQbIJ+3mM5XiE+X6FqG1WKqOzx5hLhl6BsCL3x/vvFDQvMQf5C3+oONR5kQadDg5c9/DwBo3z3DRbLCfLJ26is27hTYNhJclrhrhY2H6jCVIsZR4+MmxVUVImB8tEAJqnlwVoz4cOVHHUDmWHCvR8R6V+0Iuq+0z6iCAIyQM3OV70PbX/a1joUGH3DMUTlHj9L48N2mADIUtrYVBHCYnO4qB6Kro92wFf/LuICgAm8qhkJ6e8Lcw8rxqtIE/ZQxKGF1b7WI82XNkHCOaUARiQrPP/8O07/ze9AvfaBpEX11ieybp7h89cwBX6wV0zCKShiptX2u4q53pOwoqOpGgteyo5jFGWazFdIn1yBcIzXPy3CkpGTBIFkTaJnCOyg99loZVmJac1PPu3rsXyd1HoHQbg98Y2PZBPhX3/4Mz6cLnM6Xo981DXeVYTzdjITqAYyk42ywqELXMpccN5neDO0iSe+KoVfhMA4B5ay1Gw8asKAGocxZkgH6O7F2aVNRukQeTjbw5xv0iiC5zHB1fYLFaoI0WyLce5dPGD3Q/zRDfPwhu/ezKxrGs7BCytuDbaSNJGj7HoeSovWMs76CSlEopYWkLRpT9h6wI4A9NAO9CEtn0GqrtpfpykH6R+9lWjfO7NUcDiWdNv1VFAmXEJS4hPgsKlz165QzzPMtHyvh7V5bVBgPONv++RAwC/V6nIkSS7NQ203AECl4a2I11YHl6u0GowoBa7foQ3Oux0mFDFqix4/1qgJeNzU4CM4Fx0XIEDPqBLvPA4mLsMBUFHeCqqTS7vOWTmJ/VkuOVRUahaAO8YAIL1iLi5Nr5GXoKsdKctci159FzwIPnw0d1OuR+BXCoNZqQ4ptaSmGPmJjKCSgem/v2rst7CalXOvNCJ/kUGWApt5K8VWKYtkk2LQ+XtZC69ka5LC2sCqRnCwPvr5SZE+uTnxxib4hkH/lg7JtWuFEQSnm1oShldmhUQEzguvSVHe2TbsVSO9Hr31MiUkpa5atr5OASad4tRsWXHe2TjApffR+hb5h6CV1qkSfIjwegF586Azx//sox/Jjxd+KhPg2T/UQm6q9KvE+cRE2+HKyRMBaLMvYLdrDUL11PCCjuZKGeuvHZ7VAUlfuRrjOU1xVIbKWI7HJdjCXGMaZcYxP/BqJX42cyofv1XYEbbdPXJYdQWZupnPzWja52/cbksNtbFunclwRMP1/Cwa5Law7g6WzAHCbgVWlYfdJUDnx5tUB9ZZjiYUyhSAqHb+tziMoqRV6Dr2GMAv5dobD3SZi2fhYtvffOLXoNCVDbR0sAODLdI3TeOOOeRd4MgzVEZeIuN04Ed3WtAt0ax6TmNe7OLnG6cUlEqPOY5PiQx3VQ651ey2Aa1WFe/KGnHQagDLTyWixnjix97s4nzasAHmxTlC9fgJ/UaOpAnx3+QRv89tStg4RF27DcyjW2ZaPakFf3s9PQZoG4WKBOtftYTbYXGW1wCaPoRR1BtK795I9D7Y78iZP3GPelBEWta/5wEeoV8dCf9ea/pHG+cEkV7Y+sk2C6t0cvMrRrmM0WTRSLfrY0bc15E8zxMcfbUfwJpu6hfshSfE8aPDFZIWL+Y0WMs4KJFm6dyO3nVaiqBQx1ddYiWLT+nqXqRiyWoBRhatS+wEumx0roEFSHOqMDj0VAU24LyrhEg4AZ30z/LclvYdM4SIscZGs3HDfWjfZpHAoGXPr9EH3213DxHxoBjQMqSj8weOtgkoSVJgnGw1GKEPnlWg/+60OB0EDMV+Dp3rBDCcZ2ssTKEX3QDfEyJONP5tC1gVY1AEua+Zmh8Od/yGLIBuV0gkxa7mj8+xSYW6L1DtxiwAAIABJREFUWnIsGx+vSwFmxKk52dqN2UW5ApBAzwPPX75G9OwaYaUXx64juDbX46ssBfN6XMS3X+Oc6NnuJMkQm/shyROwInaIZJsMn55e4+TZOwBAeFNiudRzrodojBImzVyNocxDvL0+xds8vVUxyRc1wskGwVA0vAwgq/2kkJtWcTjJ4H+xRvNH/w5IU8K//j8QXk+Bt+ejx6vec0mwaIKDm0Fr2rsF0DG9gZXM3VPvE7L3sKgDCCpRN/7BNr/qPW2CfD1DUPmQdYC29tFUDzcNeFB8Yh6i53n/DYB/D8C7vu//5JO+2XvEo0+Igkq32L/LJu7nw6Q47PdTM5t7NrtB4leQHcWL83du0Wga7naaW1USH5e1PpWV0kLcw3bToha4rAKtgSoZEqY5TFeVwLKhuGkIKOGY+wOU6iApDgEgdmduQRmEdIABdrQdHdneMNLhTS7wfakdF/7e1IMdSrDd+dwdSfFQ7CacYVvaJkabrG1rzMLibQszYC3m6WbPVsrOD60t0jHk67E5cNX4mlYTVqBM7qEuR49VFMuGQ93xsTnRifJZ5CFk2jros1grmOxyOe8bWRPglfmOBAm1osrgrhxulKxNFKBndKrydfVFtFLNqzzB13kA6ulF92Wyr3ySXlyhM6T9u4Am1jnlvpXgXUGZQtNwvLk5dRtKe5/cBeTxaAcCiV5SEEM9GCZka2d2rl67n3V+CC/k4JMcaZKjqPRGFAME86HraleyDth6Vdqw9zAAlzRPowyTJNsD6xGqEEYl5s1Gt12N6MeiFnhSib127yQsULZajShfJ07cXt1TSev9w/shlGr+WwD/FYD/7lO/0fvEo0yIdR7hz3/5J8hq4ZRccqnpC9+t56gl12axk8wpbXRth9a4wQN6IZ6nGy1ebRznAcDPIwS8NWoXurI7ExUE5XhTHm5nWEh2O0hW9m+LHrXAHWlUVOwCkbXcOQzMqkhXIPnhCmSosGF93ypFcdMEqO6xVlt9z4ckxQ+JgLdIjRflobDAmoC1gIRL+td5iqIJMGsyjTjc4WbRgTyaiAsHwJAmEVgCuuworsoYr4sQy3Y4PxxXhxZMc+JbkJWWbeOeVqgZOpu0pvK+rY1seYWL9QSvixgbaY2ZNbhHsBYVAG6+z/uiPm1nYNl4eF1qjudUFHuz3emLtwCAb189x9VijnWWYFNG+HYzxWXtI70HgX5TRuguCZraR5xHztTWY0pTRVYpFpcnOL0Ycy7VoJuhRcKNS735Tl7lEbKWI6sF/p7/NYIduygLyhnxNAez0732o89Bhb4O/NUEvLmf8s0hig4nCs8Nn9SaPQ+/63m6GenvDlvlnVKIwhKn7QZSUVwZuUbVGXNrU/VSppWKWK5wnadQHRnRlT5p9J9eqabv+//N87wvPumbfEA8yoTYSO7akrM4Q9RysCrCygAC7O70y6h06vs0rNEpgk7p+VMYlYgmGfykGPGq4jrAYqUrTUGVkxLLagFGIrwu9+dfstP+i+qBG+2vshCbVl+gEetx6mui/MxvcBZlB4ENWoCgwlSUCFiLSjFcVXxPuHp0fKY6rMzfDzU1fp+wxrIAju58XRVpFoS2oQ51m0uOsvUxn64PzpcsFyyICzAjg6UUQTNIhm/zFK+LCMuWjtqiw39XCk6UWbuj9+4xKZdae9XIcFGvd+jO2xKiqnxUufaY3LQMldLVJzNEbUYUhL0zzVfxEGRk2wHLhuCqEnjWBGga7pzqbUxfvIWSFN+/eYqm5fh6NcebA9fubZHXAvU1R1qGiMIS4VrTDao8xNX1Cf7y3TP8W1Rhdn6z91ybDG0Xxs4uly1BpXxs2hmS4AK/EDWEXI+eS7gcJZtbK11GQc11MEkylPX7tRytcbVN8HUR7gmN+0ZhaLdrYWeoftAgjXN3vFdVqJOqNIAZW/majcx1nmJZxkC57ZR80uAB6LMPBdX832ee5/3p4Af/vO/7f/6BL/qDxaNMiFnLkEuOp0aVvzP6hoJKR8x9m6eIL8/w4uX3bj5hI5lkCKJylAztYpJ+9gZPa98trMNIJcfCDL2HpN1nka6CKkUxO0Bg3w3ZEXxbBPhq42HZ6vcVlOAsYDhvKWbcR6UozgyS0YagEoJKTEWJebJBFJZYVRFiFmMktWOCEb34th1FXgtXjQ61TW1ynIT7SccPtKD30CMR0O1SX2jgxKH5XyQq+H7jDG+tVZFtA9uWtEWDEqPgIs2sbtloI98N5Ti9OoWIC1gyhf2erCQfE42rKCjVrzVMhsO54aFoO4BSnRQtgEIf41brcreavk0QAa2EKgOt8lOFSLnEeaAVcD6Ls9H3yQmB9OiefNqxsN6PhdTJ803F8KyMcdZohOiudJqINfT/dzfneFOK93KAyRqBZRljFuZIS42qXWxS/L+XF/jLVQBO/gj/kHaYPhm7uQy/X/1ZO7Sdh03r4abzABCE7BxpWCCer8CScaVIDI/YjjhspXYQwEQ7ENrBDzSIKC4Pn1Ere7jbLhVUmyEnkwxiPk7OWG9pPIday/Z4qNravPlca5x+xhvMkw3i6QayECPSvR2PZE2Aq0rsoX4/RfRtDfn97z70Za76vv8HH+N4fox4lAmx7YHXReTcreVgxgZs0ZXfr+ba8iYu0EmGYp04X71snSCZZK69NIyTX3wDQLecbFSSozwCidYctwxL4/6wG5UCLmsCgENQhmVL8E1m258EG9VCyh7M88CJB+4Ro5/YOdSklUdL/BppWOD86SV40GC6mCNkMxz6qi0xmJIOtWRghI+Etm0kQeUUVppNBB9wyL2z05uRFyGwRQXyoHEWRPcVDhjOS8vWdxy5WnLT9qamjannMLXkR41xh2Erf1sNXVaBE+m+LVKuE6FGERMk9xDwtmhPaQBL/usnYKJBSN+iLwNcf/McX189ca3Qi7DByzjbczsYdgA0ipVhsUkxXadg7xrIysd6McWmjLQLCelwHjSoVIBKaWWjRR3gaj3F6WKKaVQd1BNtO7Kl3fTD6li3p33ewhc1lKRYLqdYZCmyWugqXTLt7WdEDyjp8Cab4qrieF12+OVigumrz/BHiqBpfFyuZrgqY7RGv1cYBPbrIsKbaus9KKhO8LcFoQq+38Jv2vuZRzOFOCyRlJXzobTdEfu9VYNxxS4V6n3CbsikqUyVoltaxWSFixdvwIJ6lAyV0hvUVS1wWQksGm7oUu0HKWHdJ35yu3iE0ZvFq5YMZR2gNnJUw+CkQ6UYvr0+3zrTD24qqSjqqxMAZuayg67cTYq2lad6D+EAEKLfSyP6TuONo1rsRqWAbwsC6gHZYAMriKcX1h0JCdkRlJKNDGYDJjEJC5yd3iC9uAINa5xdniC5fgJOxq2igLeuUmsaH6sq0lzHneOahTnSaFu1lOsUnWR63mraQ5QplLlB+Zn2lJ8Welfua/7cbS7su2FBNVJR1/rOjGLPIam0+4SSFHXj468XZ/g6G59/XZnoRXjIP+Rkq1RUdZ6Z9d1fgKGSXPMDr3UyOFulaGsfX33/3HUqEt7uVfrDsEnRVi9ZI/D27bmmltQ+3tycIhtYPAmqcB5IbCRFLnUb/KqMMXt7Dh40iC/GlRox8+mQKZSS7lWJyyaAWpw6MNR1niJrAmSD76I1m6hacr2BsfPyvsdl5eFXC30f1ZJh0/ou4VSKYlHr++Z3GXdz7pQDKesQMnkrwpgyBcokAr9x82EA8Njha0R3CZS2KcPWnLk1Yt6blo82rLtgtoeIjAM6GdKwdh0KJRnWud44TMICzz//bq+ibaoAVePjqkhcMlQdsJF6I3hIJODjhfdo3C48z1sD8KAFeNyPoXNe0Pf9wd3wo0yIxBjoVoqhNrMm2wbMdxbmVS3w5uYUcVCBDESjpdnJbYoIP6cKk5dvdfvUaDUCQPrkGvN1ohcl00K1vD3rv5b4FdKocFQJn7cI8wRXZex4gTZKCWSqQ2J8q0IGAB4EZchkh5R7SPl2hmVnaYJKB8aIRKVnn/MNvLBBPMmQ8HYkQWYrQ98IctvjZGXkCP2Mqr1jB4AyD6EkBQ8ao8jRQJo2cVMF+mdmIQA0dP4YWflYMKpGMzi7AFuHDytSoBVV7rdQtbWPq/UUXxnFktHvOr0JqRQw83VS1GpA2ukAACCJS4rUG+vXHgtp+Im1ZMga7VxftxyLKkLbEZyJCmdRhrlBg9pFXSo6shKipi3bQieVy83UgYOsEPewqp/5DQSlEISh7TRd4HIzhXijv2ubFIfnjRmPTXVAu2vT+vh+NQegEdNWYs9WK9LrtWVXq2XUbKeEmc3dtwUHI7P989MRh7K2c9SU95jxDoJ2Trz+Nq9FypQj8FvqxW1hjaMDY9xsN7JZy52915BXeAxcRph0CfbQ/JuYdcKtGUyiLAW+W88xFwVmkzX8RCfmbiCG3jTa9eZqkAzb3kOrdCte0E+YEH8AUI3nef8DgH8bwJnnea8A/Gd93/+Lj/0+fd9Phv/3PC8B8B8B+CcA/qdjz3uUCZGTHvNAG3BafpHluhWVwDerk9Hjr4oEleROtQPQZOc3eYKQSYTf1wjiAuGTxcH3sy7xFm14ZmSZjkXAWn2ze70xMPawaXtkUmHRVZCdwIxvvxpOgGchwXmwTRIWBLMxiThm7Z3ed5Xy7gRn2JkYJ+oouk1JBh4cnoW2tQ9W+bpNVAg0VeA0HAUD0Br6QCXQDAAJtuVlrZ7sQl8ptl1gSYcLs7uvJUPbUaRRMTqWXTcA+/+yCPE6T3FZEaheL7zCtUKhK/DOQ6U0gGbXWBowlaQCAOqI2/bPPBijc20y1BsljlBJJ9Bg23K7MyFGFST0TXlXC9A6gNhxwG5YKohd2LMmwLfX55CK4lxSBHGBYq3RpUvzPWwpEASMjF+zUttkw0h30CbIAj9CJiFoj7m/5eK+Lv2B9KBVCdqqAqVc/24IXHpI+H4LSpUWZrj2wJZv4akWWGWQixRNEaLKI5Sl2CPg2+9jK+CvBTKk+Z0wY5fd1jxlGijTGNHxpuFoGo4yDxFPMgSKgLUMNKwh6wCXqxnelOGtoLW68Y2E3jYZWpebi7AZUT8+dng8AHv28w98lf/z1t/2ff/vf+AbPCg8z5sD+E8A/AcA/nsA/7Dv+32Ul4nHmRBNJWFRWbMwx/Mnl4inG1R5CNURfLfZ37EeilIybVSbR/DLQqNRwQ7uWkMmMT0ix2TDVj+JX4NV4cjEeLctamPm6xbSMCrloe23bbL7oBA3kuCyEpgGkbPLORSO6oDDCjHKoHEPRbZOoCSFMETixWriaA5648DdgqRWnqN6WAK+TXSWq2UjZBKfT28wn6yhFMU6j1FLjigs99pOWqnGh1/74MZtwe7i78M1dP8+mBSBy8pDxSkE1S1UQbXQuwVP6ffZbj6GCXDIE70rhlXi+8Rw3mQVk76+eoKiEohEhav1FL9ZjjeHNilmLUPWMpyJwxSfXUUbYGvNJahEyhRidvuxc2PTRgkgSOeq7vuen90I4xKdolj9yy8xD/8CvfJQfH2O1XdPcX11Mk6Eu/6ipAN2BNeH88TTlo/4goR2rkKlUt+b7hpTFL/76mc4my2d0Pfi6gRXRXIvQn/b6db1MBk+M3Pm5I715UOib2u03331yV7/hwzP854A+E8B/CMA/wLA3+/7fp+YuxOPMiF63laA+DTe4OnZNebP3kE8WUAsUs1JM0jD28LOSGqpCbJBXCBgCpRuKxJqEu+ZKJ3Pon3OMT6aTYopb3FllCcOJUNBgZnfY8YVUpOArcNGpTSNY9lQCMqPItAo224MKgUsGo5VLZD4lRO9Hh2bEbq26M7dOEYMHrbfyiJEU/toGo3GHQJq7BxJGleIZUMx85WraKy6jm2PAnrO9mKywNOza6QnS3SKIlxVKEsBP2hADjgkSKWRq52ikLVv6BD7C+2wlXxo/eZkK4I+jE2r+X46PFRKGy2fi9LdVNYweDgzs59pl+M2lLU79P+HxFBhxYoa2AStFMM3qxPQdY+rSoye4/7de4C5xq6qwCVFK/pt762hIbLzqTSbG0EVImZF0rfX9m4LkpMeIWsx5HLKexgtEy6BWgsT+DtJuy5CbP78cwDA4vUTXC9mqFu+1/HYPb/H2qOWvnJIq9d2J5oqcPfGb1+/wF8s5viTMsbTyRI+b3G1nm6r0FvCqUZ1283beSDxWZzh6WSJSHzChAjgyGjtb2L8DsA1gP8aQAngn3je9tz3ff/PDj3pcSZE6Is7YFKjKNntjt6H+D1D6axKcjSNj3KdutkAuL4p40mGWaa5jsOwifG2SuwszLX2aB+bJNeB9gSMEDBioP5mtnMsYtYh4S3mojiYfAnVyvrPQ70T516Py0ogZjHSqEAYldoKy4Ar1G38OUUd78o388JuUC1On1xpwrnxdNulpthZYKUovi0CfJt7yGSHs4DjXLDRfERX+dTx/FRHXGVqd+rbpLe9iTtFHN+waTh8s2g25n1nfj+qEg9VjNQDKBnquALVQB9Wy7X1qDprPq1pMa9yob03vdKhYO0GBjCzoM5DyBQSw2GNg60UnzwyA2NE6crAAIzuG7vXdSmZS9CjBLiTCJjXo+09J1pPax/zIy3yu49B/23bpbvRdt7eRoSRDomRsfNvoSn5SQExX6PZRCNRdyUpFq+foGk4FusJ3mUTVIphGlROcP2hdmll6x/Ux91Vmfnt6xf406sT/HIJbOQp/r7kOAtzlAMwkd6Y0NHssDObt86A5XbBTdTrEYkK0/nqQcf9oOg94PGgTP8L6FTgAdgHDhyJR5kQAb0YCKYdGo7NuwCMZj5Dm52hWHMtGarGRyLpaPH1mEIQF5hP17hezPZ2nEOAjp2XWRqIjbOwMAtnhEpRrBu9wHKiZ1n0lg2bdWw/O8ARtEGMqv5F2GB54IYeHqvlaVrE4LHFVyqK68UMTcMxMTdoOMkQf/kawfUEba2FiYcJkRNdqTJCt04c94xSMqdFq91CtCAzoG19KJNGbYghu5lhnSXIa4G8FgjWE8e127QMM65wfU8BbGv3NYxKAZnsIM3azjwgYXpWplvS2znh5oC7uX3NlDdI/EqDmwbO6kUZQmLs/A7opBjy5t7mzdbSCgAqI0U2TIYWmGRjV5nF2ozdtSG7TwiiX4c+8LUEaw9y+2wi4ZMc/vMVgoyDfPUM+eJ2z8j3DTu3y8sQ1UJjNYYi29oxg6BufOTSepce/5z/19UJKsXw83y8TktF8S+vnuJVLsB3bKoYVUjSDOHkzq7fe4fn+2AvvvzAV/nfP8qxfGj0ff+fv8/zHmVC9KCrw4DpQTs94sHmyMEHkuJwhtV2FE3LdTUimfZZc4TvDiIuMFMERRm6JLCLWB26PAznF5woXMSZ0dMUWLYWoLOtEA/FzFcapRgWI4LzbhCqEaXzoHYcvoOPs8dr6ADKWlMpimP6+ps8doat8ZevQb/0IZJrnEgK9defa6scRcGI+qBZGKC/D6tFO0zUUlFkmwT0nf7Zcjl134HqCIomQNYE+GaALo1Zv0XvHTkfgujK2y6G3BMajTpIhoJ4ENRDOthn6GR7XA2FmVZ5wKSmvoQlRFwaWL5O9scqxYC1oN3dc8Uhd85qdw7bkEOk7rBFPQxqAF+3bcicDKGhhBw+lt5V27vRdt4WnOT1gKMpdabDs9/d6SWFrHwE8w2CX2zQP3sGWuSIoTVMP2ZStC1iuynqOoJinRzcYNtNmq7Cb0+Iqgf+7GaCRR3stc5f5WO1IM259TSCfJI5V5dPEX3ToH319Sd7/R8yPM/7ZwD+l77v/2fz/38M4J8CeAPgP+77/teHnvejJETP82bQvd0/gb5y/kMAfwXgfwTwBYCvAfyjvu8Xnm78/pcA/l0ABYB/3Pf9/3Of92EGFv2QsKAOjRatwEmHs2gr7H0MTGLh3EM+1JDHZOOQwg2gd6GfRRyLmmMewNEr9j4T6UA7z4CG7haUHi4m+ua7X2Kympy3RV4LYDHD59MNvFACLAR8D57hh33sqBTDu2yCxK9Hkm5SUazMQmjFxK3hrRVWBwYVX8tQgaBV+mcnAXAS2BapplrMg2akKvSL/5+8N4+1ZFvvwn6r1qpV8x7O0H363r733fsGHg7YRMGCSCGRMAoyioSDRBQUKTEOCiEEk78IKBLijySKRRKBIx4Bk8QmEbKVOAiIZIXEDsQoCHjYxsS2bL/h3ndvD6f7nNN7rF3TWrXyxxp21Z7O7vFdOp/U6j59zj5Vu3bV+tb3fb9hsACQdWZm6LmZ2GhagpuaIvT07zoLK9Mm7lsJ7fLOCxOdHKNcz2ABvdAu8sQBk6y6kG357Zsz+l7fQNq2bi2H0iJF7ackFHmhxWCX5ufzRtMSdy27s8/NaBuGcqI7ENGdCeAzqHQAiAYkWjyXeXVi5v1RHSCkARYNR0gN5cgkws2Zs5AUxSqCFEzTiSx/WTDkRYRK+GZzcNx7/n+n6+RnUc/dsF9nvu7yeLeMfl5FvC08RAD/GoA/CQCEkAsAfx7A7wTwLoC/bP69Fd+uCvGHAfzvSqnfTwjhAGIA/wmAn1FK/RAh5E8B+FPQb+j3APiS+fPbAfy35u+joq458lmmh+8NQ72MsVgmTnW+u2vejMyvcRovXfU1mQ+wyBOczjKEyWqnN9lmq8uG1e20VdfmDMP3WqS+wPvp4STEiELEpFuEnCD3LYLSNjR/keFBnoKZ2Zow1VcpWa9aFqYythuLquY9gWxbRW6GMqTxRRFvfc9WJHZ2J5SdyZHeDLExwA5gE+bP0JQUIRVIW+0tKbDedHSvv0WrWjrEiGvYOvM4rssAkuh5TugpnIWNWwitKexmfHGwQEiTXlU5baipCPphk6HmBEo8MMo+3TalPdc+elH2jHBbwRDNCzy6vNvboASGZtO0FL4By6zft+ZKUqnc18KgFpcNW9NFjDVY2XoITQKwiSA3vMuks0h3hedtxWnJ4owo+GWEgAlM6wCLht1KJLcEftqpVncpw7QNg5Ie1AGEpqo9iJJrlLNcbxaqjq/kZjitY14hNZ0EG9byabLhzWkNvqWg64Ro7tPu57MUEl+bM4Q0c+IDWomHGA9NOO7liAMjX7/vzXsp9BTGQQXuN1vgsdcRb5FSTaGUsruH7wXw/yilfgHALxBC9rZT33hCJIQMAPwrAP4gACilagA1IeT7oAmbAPBXAfxd6IT4fQD+R6WUAvAPCCEjQsg9pdRjHBFCUhRFCDpP0UqKfJa5JGBjV1Ichyun39n9WSEpnlyfIl4mGI36A24hKapGq3VY3z9KtfVLmGjghJSee1C7YQEcp/z2GRcjak2KlvRo0V/nrScJrioOMRthWQd7kW/SJD5byexL9lvHWfU5hjacI/zmztssELkgSFg/KTZmEexWa1IRJ7DAPOm4o/uCEs1LPQsL534iWg9XFccpk7gXF24O278vNtWNJD7M5r33Yr0MrXi6VbdJfeFE2EvhY9n4+pq3Xk8rUxp+G2USPC4Q3pn0qoC2YeAT7Vjx5PpUv2eqq8vWSNdVncfYJi3fKMZshqXrlFILzpdS64dmvt2QUKOM4u1NZqL1evPRpiWOyxhJgWnNUbYemrZPY9kX3fO0Jtn26diF7NwMUtdQBUeVx1hszOUsn3UzuGlXU9pq8FURIWCNERq3aFf9GS92eCUeE99oZsBkiPPQKuKY96TW/04YMOItRkZcYNEwlK0H2epNlV4XDiv2vLJ4A8T8NxmEkAul1CWAfx3A/9n51t4P9NtRIX4ewBWAHyWE/BYAPwdNnLxrk5xS6rHhkQC6xP208/oH5v96CZEQ8oehVQhwh8c4TRY9hOdykToqAKDdwnPhY9H4uC4DhFRbJp2FBUZRDuq1iMOyVxnZdudNnuEbkzN8oQzxzoXWOrXIxmUd9tpVnDc9BfwoKTSJt/EdNaMbmpu1nnEBBnVn3otN3pZAb49jW4hpdtgYtmz1zh8tUEruqqHuztwaElukrK0Sj4paQVQc8zzBrIyQ8qpXuQozt7NVlmhbCE8vzNTAortJEYBDmm7SCKbGuf02exzbKrUKJSHVXna+SZQjXjnpNOvdaMW5K8HcdQ472qK2OlsTu7mrekdc4jwscZHO3Htf20N1gCwGICQFRRBL+IMcdLAC4evrRWoPvvQQJivEywSrMnTzXgHNVRyGBZpViqIzJ7T0GX2Nvd4mRLZAqTyTDNcVeuaTvTQTG6WkRluTIGN9YfNprZGUhdBJ1ff6tJZdIVsAXmeWeeBn24btHVnIkiOfp5gXsaN/WC3ZzbDJMDKzW8rW62P3XqeyRcD2V5g2uuo3jCjETCOkBzLCtBEQiiJlnrselGjBeN8DzsMWGetQThQBBCAJMWo90qhs7VfseWXBA7D7Lwuq+buv4kxeRfxZAD9HCHkGgEIT80EI+U0A/t6+F307EiID8C8A+EGl1D8khPwwdHt0X+x6pLa2rsZi5EcA4DcNBmoX3cEmQ8vladq10gugyd82GQLaZNbOBWvTIpwWiRPpXtUBlovUJSFd+TEIQhGwBtxvXDvFhhUkjsMScuUdnNNZVY+UCY1GlVqqrWk9Zz01DFfgvkbTWvuZahWhnmRgTYFylmFVhq4q8YlCaA75QVrgg+EEpdDcRGA9e7LVsW2N7otVHWD+bIjkwQlCeoP60RDTqxNjbaSvd8BEr9XWDeZt/27ZwoE5PkxzBxyy0bQUsyqEaD08WGptTTvntapB7nhH7Hg178v6VFKURvvWLoR0j7JIJZgWzy51gvA9hYtQ4F68wt1ksbOFnQtP+/3VAajXYlWGoFS6DsKusKhKOx+tG79n8BuwRleii9sBJZbs3RhVHoeaNe8/87evV1fxphDUVZWAt5UUbUKmRv5uM8rO81BKstWWPSbyeYroZgj/2Rxe/BjqZoH80RcwX6YoGu66ANJorNp7316/F4lSMqzqQNO4jC6pBddQJpCl2tYp9RskTCGlHkKP4zyyddn2AAAgAElEQVQkGHGFXSAbnyjnPdm9T3voUq/FtA5wPR0hfbpEsIyxPYh4NaHqGs2n33pNv/3NhlLqxwkhPwNdPP2y6URCKfXLAP7Ivtd9OxLiAwAPlFL/0Hz9k9AJ8YlthRJC7gF42vn59zqvvw/g0aEDMF/foIsdupU24lA7YcyM7VFIJRLWOCHrVRk6F+6u43vXhHffzE4j56irADajK0gs2t3SWzasFNxpoo1FozrApIyNzdMKgyQHt95/sVZtKeYZZg/vIogLXF+eY1bGbsHTLRjgfrLCl8+e4N69J6jLAI+enuPhYqSdwXnpNg2e1+4FAgGan/Xw6g74N2oM5gnyyRAPr+50aCtrV/J9CYoRsrOS+DDN8eWzJxgZdZq8iNz8UkuW6aSgCf4nDmhkY7kh1rwrhKn0bAu7aLjbdABwrcfugyJbD0XD8dFi4OZCvqdwyqVLhru8KgEtEj6tfYQ0dMAlISnSwRKyCMzcdMcczc5uzXnaey9gDQbGY29pdEaPNRTeDCtjtwss9LzhEwVJ4NqyXTBXV5LseaJttKj8dD4AfdyCxSWCYoL65gSLZ6OD9ylw+3N5KJaND6n0ehLyGkCDVlKnVgMAg3SJcZ4iyxOkvoeTAPhitsKI12YeGexVKto0Dgf6G5GPZycQH1PtZPNcZ/580b4lLVNCCAXw+wD8bgCKEPLT0N6MBwexbzwhKqUuCSGfEkK+rJT6NQC/C8CvmD/fD+CHzN9/07zkbwH4Y4SQn4AG08xumx9SLjC+9xR8MuwN2TcjDksk+VolI+2QpAGdFG1bczNpbQ7/eVCD+w1SXrmfrRsfIa97oAnKJFqpq0RpFrl9u1bqafTpKMq3FCq6At3JYNlzLG8F02odz0aYLDJdtSriSOap3+A77jzGB1/6JqI7EzTz9cahEj6ioOoRoi34xs4SGZVYrNb7VNl6+PTRPZytIkzmAyyNETOwrhi6bTvfU8hYiwX1kLYeUl+3j1xLyQPOgxr3sxlOx1MM71xDVAGiPNJWU3mCEbRM2nUZQSjijJmtgz2wbs/umoWxjqYmAKd5OzX+c8B6bkkJRdDxAWlaio+XGb659N2MbOS3OA8rDDtqRTYCpukboRegJLrCuioDAy4iGLYU43l6K8dMSOrOE9D6vHFYIs2WkIJhtIoxNWLVNiz3U4OwqNPOhaccgIMR7UzRtOuNyb6k2L2W+7iFvqcgJdFVvhEvsObY6wSJvTNKW3Futulb6WE5T/FkrmUX+cMKySpEMU9RFOHO50ifX+ssrmTrHT0L34xCMFwXiaNzbSrkUNq6z/okYPhiVuCLo2cYpwvUjY95EWNSxj2nEGANNAPWLXVqcAJdBO+DxRBYDPFbX+jsj4u3aIb4Z6ELqa9Adxn/CIAvAPgTh1707UKZ/iCAv2YQpt8E8APQW8j/mRDyhwB8AuDfMD/7U9CUi69D0y5+4LZfrloCUXHUZeDmf7tmYHWjB+6H2mo2Gdr21KFq7uz0GUJeY7LI3Gt3zTBsq0VKD2XNEbDGtQQPzW+eJ+oywGQ20O7tBrAjzLzww9On+OBL30R87wY00YnW0iSa1iRpSR1lhVHpAAihdad/cg6xzNxraNvi8tnpVpsK6M89GQCYDciIr5F3gF4gfbJWNFlWIepa68i2BvJuHQ2o1+I0WSANSvzqszMjXA74xEfqa16d9XaUUs/5KsHge74Dy5yFpXMlsQk882tkfu3QhaL1ACujZhLdZZ7iuvRxXQKpD5yHCiPeYByUvTljN94daGH4B3mCaU2RCw++t65oFnmCQR4jrRnQSdSq4GjmCZbzFLMydjZQUhG3UbGz8cogp+21TnnlZq4pE6YaJp3367nWqX6v6wKVksOAmO73dlEUQmp+mUe2kmHpHkXiPnPL97NUomFYIEtyDUIzYvFVHmMyH+C6jPQ9OdH3rhR95Hb3GWVUYuj4jWtesJ3d7nJKcVq75vnttjDPohyB32CRJ4jiwh3fGlz3PgPDpczSHEWhnU60+AXVlJeNdedQN8NW/sdoob5oED+Af/+D1/b733B8L7R+qQQAQsj/BeAX8VlMiEqpfwJgl6vy79rxswratuPokA3DzeU5FnmCmzzr6SzasLOiqzJCIeitZqTHRpotEUWl008E4DQ1uw+fH+jKMaxr1I3vdoTAuqVED5B7nzdCKhHyCh/eucQ7n/8E8btXIFENL5JgWDtGVIIhr0Jwv3GbiOF4ZqyeKgRjzfeiTKL+2Hczu2NCU0Zs4vUM8Z3iugyM16CdPekV9EGeAo/fxel8sPW7uN9gPJyjrn0sqxCLhiEXHnYRwBfCQ5nrmWNoEh3zWjeX7S54lCiMohzDsMCTPNtr+qx/h/buOw8EzsISwS33kE2KzItwXfavm3VTaBYxfOj2oCw5qkmGqwf38PHTiy1Pz+siRiWYbiG3FJ8uhphUHJkvzIxZJ4yU60pmu13nY9FQVyE+b1APmgJElHaHOCIp7gtmKjk9CiicZ6GU1Hlw5vMUqzrYSiTSCPC7zsTGJtRuUnZVkFoKkG11kTSPNXTAO0YUxkGJs4FGlteNrwUhqLZ/KmuOtvW0rVuzdmeJw9JtIku3aWEocbujyZsO1VSo35IZIoDaJkNA5xFCyK28lbdSqUYIhuvpCDerFI9XCVK/QebXjltkw865jnGKOBSUSfhBDR4XYHEJsQrBwwo3130nAZsUrTuDLyh4VYMb8A6w5mY973zlUDAqMfZrvH/3Enc+fIDo/hVIJEAigEQcRDbwgxrUazV3T2iX+0GSO7moYLwAP53DOxEgjIDQFnfyyFk13Ra2LW0h5F11nfMywuNVgquKI/TafpsoT5ELH8OgdC4cWbzC+d0rJKdTiJI7yaxfn/fF2pt2fS2lIvh0FeC9uEJIJe5nM4xTPZctqgDLWguej7OFa0Emk9IZ4u4KzSHT6OTbqC/28313MNFtTJKsASgmiddloOeI0FzO1fUY15fn+PTmfCsZ2lg0HOVihKbVsnHStca1Ek6X/L9ZuQtFMOIeFg2B2HP6+5SSXGVn23qGLnEoKaJVkN42146a36Hn1xWyaIVBugSlLaRpk0pBHWhmV1SN79rFVmA9ZA0C4xEKoIcUB7YpHY4XapLhrAqxaDRl5iwscSed4/RMOwct56mjeFga1bIKManWm7vQdFaiwVJvIGuNSei+hxed976OUEr/eUvirxBCxkqpCeDEYP7KbS96KxNiIxkeLkZ4vIpxVTFkzMeI9x0hdrV5NsOiLW9uccV4lWEX8IQpvBcXeuHOFjuNSBmVPYd6coRp7a4gEZC8c4V3zA4cgEPUpXte4/kCYWJ28h0CvqUBDMMCyyrE5Q5y/vNGYnb4TUsBATzPpyFb9NqCQIAvZfu1X21QJnA6XhPkN3fzmlCuE0bEhFPP2Qe02mzbu7m1v65ebHsQAPKbER4/usDjqda97EoMWtRrKZkBFHHXSgvp7U7z3di18bLz3FMuMQ7qnqzbiGsFn/VM2kjm1bynfHNb6JasRuWOeI3xLbZGVht31+ZVSp3ArssQpfScwDo22tfMUG+yNO8pKXWR4N1kuBZD14IBu65pXq1nl3b8YlG8+4JRI+4OGO9DvnV/WaNiu7mxyPbXG+StcbtQSv1FQkhgqH0KwK8ppb5y2+veyoR4W1hOm/Rat0Ob1hzXqxTBZN0qzJLczdFu8uyFWhxa2Fu7LmxqILZSi34XVYBZFWJac8h2nQzfM1XMpvxc02paR8BrBHGxMxlaXqSlEMxkCDy5AABc+ALBOzcgUUfzcrDCyece4d1V5GaBwGFiNAsqJFHhOJU2skgLnhdFCDy92EqKW8lln5KI1+I3nlzhbDRFXkS4OoJWsBnUW1s4Haq6d3223aT4aDbGrIzQtBSXRYircg1+CKk4mAy7x3i6HGBShe4ePCY2q0/LEbXzqi5YyfLWjhVr0JsFBaEUGLHvCTgPJM73eCGOg3V7uHuc6zI8aqOpXweHyj1kmGujqzojWg+VYFiVIXheY1VEhiZzuNMT8BqD8QyUSZT5br3YTe6iBV8d81mlvEJahQg9/ygsQMp118PpuXa+Z5NhyiuErOmZDbzOeItQpr8TwI8C+BZ0Qvw8IeQHlFI/c+h1/79KiNbLLaTCtU+Zp+d3TUtwXYYIaYa7gyk8r3UPj1Xcv8kzJ359bLStVsHnVk8zqF3LVAo9e5gWCSZVoFF+HlwyPBvM1iThToVoH1jOG/B0u9ppDWigMsnQgkiuiwTN43cBAO9GFXg0AyKAMALQFiwtcH7/MSiVeHJzehCNR7gWNrecStZIVI2Ps9EU4/NniM8mqBcmET69wHSj7bjpWr45F0r9Bl8+e4IPvvAxkneuUN0MMXhwgYdXd7AZu5KZ6M0FDQhkT77qyuhteeQxgdFgjnkRY1kHuCwiPCooSqnnhxGTCJg4SjbPJkPmtc5IWvMlb7+nuuIASaBdMgLTfisldbO8sNOO3uVn2X/fWk3G2ljp4wAnvMVZWOEsLByIx3o5XqS6Y2HPvavdKxXBVRnuPNZmjLhG5Z5FGi2sqS+7r4MVsujOAJuW6ll30WCxip1Ly76IowKDkxniswloVCGcJyhn2d7EuBm7tGdtdHV1x3WAyyLE4kCO736GkdS0q663JKATcebXzq7KSk2+rPPIoSA8AH97QDX/NYDvUUp9EwAIIV8A8L9Ac+D3xluZEH0qnNcgEJoZYuN28zYoUbiIVj0rnOsyQhqUOB9OQZkECypQFm09DFaNnxt7KTs/3BVWPg7QxHyPMnhMoK50dWhnH1IRh7jbx2OzKE4AzglgqzoU2rdQ89L6H/GsCvHJkwtEXy9wHn4dbKhfa9VReLZCNC8QLauj4OnUtOeEpAj8Bpw3+ppFFVjDNEfSqAL5nsTILIDTInHJJ2ENEPZ9+TK/RhyW4OkKbLBC2zBESYFkfpxB6qZ7wyEFlixaOT7fLqUfHtS4GD/D15/cQyEozsMWJ1wr0nxxOMVpsqZL7OsiPF0O8HiVgHktIq9FaLRIAeC4FNJfSANDCYn8GqFpp7GN+SuwTV3YjFICC9nAh4fQ0/Zx1DPygFSCAaBSV5wprxwlx0qeMQMooVY5iaiDbdPQU6Bc02NSv1nf5wIAw9bGwgJWhEkc9t6XirhZdyV8B36ipoV9b/Ss9965AYXRqAIxnqYsqEArDo5a06Z4DV7oa5xXIa5XqVbP2ZFoKZVOCau7NgzDAhdRCEq0WAQP+mbiAJxoR8BroOYIWV8pClgrUVlw23k22/lzrzJUVaH65K0B1VCbDAFAKfUNw008GG9nQuQN3r97CXrVdw7f1/Y4CwuHJhPmQeuGFMwpgyytPRPXgrtRUuxMhl0TW37gJhbtbif354mu6HG9iDGfDDFZZjsrj6bVLhBFHkGWHAz7FVJ2Rq20CaAJ605/W/K0gImz0XouNy26lkwNEtbguoxubQ1dz3XrtKk4pGC4Wgzx2PAirdC07gYolObSr8S6ZVpKDRzqJq+7ZzcHHToolbg/vuktyu9m01tl4wCskYVSe0Eyotw9ZhVxHi5GqIwRNec1ridjPJyPDUhGn5ddSOOocAst7TjYu3MlSrtimJnZbdVroyRAYJCmO4QTTGIMWHP0bPJQjHyJcaApIZtUCebJncfYx9XtVY2KYMQbnEU57r//EABwfXl+1KiDMonR4BkGkqKpOIo8QjIvUV7f3fl8Uto6cY1uBKzBRbLUm/BotTUm4X6z9azIDX1bG4xKp0AlJUXIayS3zFpfJhTeKh7iVwkhPwrgfzJffz+Ar972orcyIbKgxp0PH2iNwadyS0i7yyu085gRgJtVuqVuD+h5XNX4uC51e4V5rd6Zm+rwUGX4sj6AzxP1Isazx3dwPR1hVh7XCnqRUEJB1cy509vW2aHFMqQCZ4MZxufPev8/LRJEfo1BtILntUiLGE9uATE1LcXj6YmrDr4+G2nggyV0byQAy1FsWg0iEYr02nN3714hGc/QSg9VHu90MaFmcXp3MAEz1QGj0sHtjw09s6MOuWqRn6L18DjPcF0kCKnAVRlhUnFETOIiKhDwCnFYIuC6mrELbTBvHEpzM6xLiUX1vmwc0xbeDMd7dGLWbW82uekN2j2GlN5zK8qMgwrv373E6EufANBz7mePt9vs3YgHS0TnE9CwhpIeZBEgWYWIJgWqxsfXJ2dbr6FMgEoPoVWc6pxnyBpnTt57jRm97Gu9Wn/IxvhThqYD1a0yb9Mqfrkgb1NC/A8A/PsA/qj5+mehnZIOxluZEOG3CO9MkC1jjIsQ8zzpaVXasC0oC8sO/AZpEWuftKjs2Lto4+BpzQ3EXsu2ca7bMN2Wpa3WLDdKtLdXTxETzi3gRcMlw8m4V3m592qqjJDq4T/fYXJ6W6iaAehXRPKI6hCAUwHihswcxQWiIoJoKZKg7AGYLBDotrAzOcC2yqQzv3Xn1+pEaN0FKIGxLmIoqgBno6mmlZzO0DYMHm1BV9HO2RKlEuPBHFFcuITUVNwJBhxzHey8dNmB/9t2fWk6FJPax7TW870T7mkUpqmcrFi8VTzqJjv7vo+pCrtxwkIwAidAvRnHJlOtitNu8QAbs8hnrEXmC9cOBNaV4XYypFtgspA1qARDSBlCKnoiCBETCL0Wd5MFRufPQC/0M53xh/Boi3oVafCZSUqESXi0RRAXCMdz8NM5SCSgag80rEEjnbTP5immReKq7W5Q2moDciqdJXRXecqak3fDjhc2Q3RGIdacOglKt/npGgS8znhbQDVKqQbAXzB/jo63MyG2Wv2+qbbhzJvhea3zKrRzgTgqkAzXcyFpVE5K6bnEtaxCzJcpknwJvgfGL1sP16vUKV6UNUdS+07hYr7U3zuLchSC4aoMULZW/DlE4Deu8rLViG2/bS54VR5jVURbaiaAbkfa2R0AB4po5gn8Z9rOSFynEMsIYhVCiuOSXGsIzQ7k4xuH8x2+baVpBwpTfRWrCEW131l+UoW4mo0wfKotj8pZhpvrE4f2tS1u217dpAfY6HI6M18LTjtpt1tcDJ4numLbm1GaGZeuDgnsY7d5vtPax7Tx8KxaV7S+5/WS521h35tDCXcUlpp2/buEIriq9HlchF6HlrIdt7k92OiKS9jQwum76R3ANh3FPiusAyDr3otWZGAYFk5ogxqg3HlYOSNdG96JQILHCOaxS4aApg11OzsyD0HpCpCedtUw5HrOGzcjDvj+TWQUF6BMuudBGjBdU3F4VPsnWh6iu0/MbLRouNsUUaJwLy5wkc625BpfdxDOEbz3uTd6zNcVhJBvYkf/Xyl10M7j7UyI0kN1MzyYEK0UF6PSKUnwkIKXwVqebEdMay1YzbwWbDEEe2IoGhfX/VOQa+TddZEAphUWLIRTEKmE7xBqF60Wvr4uA1xVHGypNUi7/LZK+JiUx/H6uoi7UZS7JGj9GQFg+ugOaFRBCQqxMlJlFUdd7Ze7s9E2u2+dzR0xoBfUZR3gajZyGqmT+QDTIkF6YCbyq8/OUAof50/OHXl+V2hunF4o8w7AohshNclwB/DkZaOqufPB3IxVvabUlK1nPB4JGuVjzJudSfxFwqoA+d66YrRuLZVx13i8ipCLrg+isSAKWpQtcVSSne9RrN0edh7fHDNioicwbqvD5wlLnrfSdFaJB9DJzybFblAjS7jLSNc7EWB0CTnvPzuErS2VpJGHswpBgE6aPKycEcBmxWojHSydsL5Fji+KWKvZGFGBuuJOdtC+R0tXyoUWobfJ8CxaIQqqvcd7XaHqGuUnn7zRY77G6CqhJQD+TQC3zpHeyoSopId6FfU4dNR6yFUarZYLH2mgZzK2/dVK6v69rz3RKGKS4touycZmUhRmVw5otfxSxo5fdD9dwPcksnjlhvPLOsCy8dEIiseFlhpLG95T2bHu74eiaalT1r+ICmTxCmm2dIo6HpVaJqzimD+420tiUhzvBNB2qsOdLaWoAqWt41otqxDXkzEAYHZkYv94PsSsCh1NwR6LSgZGFM7DEmdRjtgskqyMQYm+jvpaK4RUm99mrO3QE7YBNIf85rRqyvZ1saAiOwuNO4v1qg5wXSS4MoTxbuSCAOgnxWN5iXvPseOBCJiqFXCk9cuSYdEAy0bLzoUUeC+Wzpw2YRzT2kPotU5iD9DJ8PEqwViy3vvbDPu5WLGBae272aH2Adw969yMrsVVNxm697khw2iP7SuCgNfucyQW/OUz0AjwogXagkIVHFKukx6gRx2y5Ds3eoc2hjYZhuO5E9ZPVhEWRazNtU0S3NVSt+o2do2wyfA0Wbj575sM9RYZBCulumCFZwD+K0LIzwL4Tw+97q1MiK0R7q3rdatpPJjj9OIK06sTfMNw8Z4n7GLjE6Vh6ebBXtYB6DLTNzCTiM8mBmxirH1a61JPTSKlAPSO8H6y7LWHpHFnsItIKa1LAe8hCR30XKxbO93ktGx8TI2J64hrrVQp2M7qLZ9rLRpKW4eyrExrFtCVbisYlKB6xiYJSK00+EDsNmC14fkCd778kf7i0Tvax7CMe69ZViGYJ+EVWrC5qALTntZVxkJQXFUc50GAe3GOU+N7GLIGqfCdPNeuSJlAygQyxp2qSsTEQSI4i0u00oMv6Ba4xnJB7bX0qJbjWhQxcnO9AtYgCiosjLPBovFRCKpbt+5zXSvn+IThLKw08lTSWw11nyeEpGCwaig+pjVwVbV4Jkr4hOLzcb9lfR7UyBjFvXjV+x2TKjTOIfpe5mamdQj0onVqG+fkIIx2rb2PpSJAS3tek90kJw+4wGyGft12W1kJvTkgXSKIaYkS2sKDQIvO3L/oX49uctQJql0L84vjQGtt6/WSoWunGpEBqYgTlA+Y6FF47LVtJd2ymnpd8bbMEG0QQjIArVIqB/AXCCHeIQuotzIhqpagzGMH+BgP5rj/nb+O8HM3SB4NsSoiLJ/e2/narlWTrRLtTk0rgSjnZP08nCBtJcRRSu1ULhTR7b0qQNt6mJUxJlWAm5oiNOTbTYBIN1Z1gMUyQXgzAmUSs8lwaya3EB4e5DEyfwzuNxgxAY9SsKC/y5eGyI/ah5RUE53NAj9fpg6AwxsGJT0Q2qJ4OkY+T3sgAmE2It3oJsVPLi926p5Oi8QBgR7kKUpJHeKulMTN/QImEJlzb1tvbyLcDKuGYtVVNgW9N4OFNVrTXbBJUW8W9P8VRYii0Ma+k/nAIXoZlQgM+tQu7qWkrk1p52hN57bp3ktLwdb2TN+m6CZDSwmxXY3M1xZn1sz4UDfhLFoh5aUT0bfXvDuPtNxb60bRA9UcSIbd2aH9Wd+TqPYsZ0ootDMPqoh6SW5XUuxG29n0BbwGDyv37Piv2HUi5ZUz5QbgZo27wEV3X+mR1+HxAOH777+m3/5mgxDyOQB/DcCHAMaEkL8P4A9/5vwQ30QoRVDX+obKkhz3v/PXEX13CfGF70bwrV/H+zcfYbGKd4IqWvOA70KHMa9F6vQF9/Ma98WI17rqaRhE66EQTLfaRI1ZFeKq4ni0Ivh8qpAwfRyrrrMZTasTV2wqvHmebL2fptVJ8dM8RcAE4qjY4kV1W4FVzbEqQyzrUD+Mnp7hFCu94DcVR1ByeLRFMc80SMZWtwdAJZtJ8boOtqq0aR3gow1DZ+eb5/RCSyRRnzdpQQw9sALgKsHuDJZR6QyPN6k4NgiT8KCpO+467kiKAJybSmMstqyaSWCqckCjB63YczcaY7Fkkw2wBtn4HiCfoxA4FvSyL5jXIvM1D9S6zWsFnLX6CzM80iQqekmh3qiifW+tZhNFpZYQrDnKmrvrblHEeuOjZdh8vm65C0lBvfaoCvEYXmQ787bmhzZ2JUULqhFV4MYulOpxAwvNeMU42LxspLxCbDjN7nzN+25bD6uO8s/rdsdo6xrFtz59rcd4g/GXAPx5pdRPEkJ+HpqC8RUAv+fQi97OhGhuqIDXDkACRqEYh+J6eE69diuBaF8z/X8e7ds1nWczN+N7FdG06yqxlAxXZYjLwsNl2SDzfSRs/0bGJmLRUkyNNZJtMQlJcRbapBGCEoVCUFwXMdKbU7wX1AgOgIY2Q5qWj5QUkSgRJCuwuIQ3TzVHymixAgZyLjQ/0YZF9nkQyE6mOC+0O0bIGrfT//rkDF9bxPjaHBhxgpNA9VqHhzYedA+MfXOeBqBncLwvIW79HiZBWdGjYXBu9DM37oUtlRVFsBBa5q37fnYLaks386TEgoCAjGkI/qHZ3buDyUEB+pAKnIUVpk2ERUPgbwh2MHOtLF3jmETk0dbJGlq+XRRodaPT8bQjcsAcF3JzhlZKhom5d7rWWZbnGfAalUmkVsv0eaN9xiDmMZp5AhpV8LMVCBeQ+W6A1joZ6kTPwwqx9BAleiNgZ47heI5wPEc52bYmA+Dsq2zYe2+c5MgN3WjTtxHQSc/zWoda1mMX/XfR8Jd25rkt3pYZIoB7SqmfNP8mRqlmm1C6EW9nQjR/O2Hub7yHwfwZkg+/ivzBOX7l578Tv3x9FyNeG/k14zggdywEFXcqERfpDJfLoXa8bj1YmMeyChHkCfizNTdtkSd7rWps7FOo6bbUuk7zFugQUmEW0N3JAIBLihbtF5p23qEZRBwVCHjtFP9ttK2nienJCtkXH8JLG9CoQvv19111WVQBqsbHo6fnKIoQdwRD8s6V+x2EtmBBjeF45tCPdhFIl0OEVLcKrVMApcdV37ZCtAvm8+yidy38dlaaT7QaTnbnxn3PJkVKW8RR4UxpZRlh0XA8yTNNsagCTIsEj1cxpvXxpH2bmDJfuaR5FjauVUmpRFFo02TOGycHyP0GdwdT5yRvDZup1zq08IjXuAg1VWUpKAr1amZRlAknWtBFMO99j51N5rIOdoJs4rDE+OyZE+EuVpGeay+tuo+/t11+PR0helTAH2iaUXUzRCs98GylZQ6jGoS3oCi3kiJhEtgA1XDR+LkAACAASURBVNiOymZnBQBYWiBNC9z82geoTBfl5voEwqwX5Qa3eDycI0xW4LxxwvvLOtAiDcsB3hlODl671x1vmVJN74MkhPw2aIP541/0NgXnDcJk5R6q+dUJ5lcn+PTRPfzS9V0w43TxdDnQC8Zg3nt9bWTBujHOFqBei5s8w3UZ9ZLirIzB5vphXxUR5kV8cDf3vDu9pWCufRr59VHE67OwcG2vQ+LElGobKWtaLCUFFujtyKO4wOjDh6AfcqjBGTi/xLB40psj2UpxsshQfsTxAROI7kzWVaIv4Ac1zk6fucTsUYmz2QjhIgUjz3c72mTYGpBCaart25C4duddNHqz03Y+57ZhWBjuox/U4ONF77X5bF2JDdIl2tZzxy0EQyFSpMbSZ1rT3ubmmLA+gyHVreKU6dmnJeXXtTHNLShWZYgsyTEYzzTowmudI8gmKMX3tLPEQmSYVAxF/aoSokZDdqkHrWCoVtHWPNmGTYohFWCkX/XZZJhdXINGFfjNEP48RT5P3Zjjo/kI99MFhuH2+lYJH0+vzhAmRW9WTpgEDbUhNjgBIHYmxV1hkdmWzkGjCmy8BD1VgM+QPJ0hnwzx5Mk5AH1f8KBGJBiKIkRZc4yHc2QnU/B0hSBZoa51+9gqFi0bH5iNv71J8S1CmQL4CULIdyml/ik04uq/APDv3faitzIhEpidqxHnRh5huUjx0dML7cIOPc+zZrVzY0+UJXmvWrQoVatGYaWXrMD2wlSApaTwBcO8iN1i22y0RGwIQ53YFymjyPy1H5potRjAwiwu5zvIuoxKt/jZxcapl1CJSrCDCYKbB54FFTyqBbk3E64f1GCDFdTgDO3wFHQ+B0t1EqUr2Wu3VcLHzSpF+K0aFwDie+sqyyLluoi5cbrAh6sUN/XQeQwCOMhf61WGxgnBqttYAI1ty1Kv7c1ouv/uVpRtw5wAQBwVeoE3LTLPF+acRW+j5Hltj3qjuaShRvm2a4I9pUDMjMB4C8CDSXx9i5+lYKAeRehpVZezsMB5NkOW5r1zXqxiREHlVHNaSZFEhatOuzqh3RjzBvfiAGXr4zzcbivbsKbSlkNaYr90m1VT4amuwiyn1VZNkakigT7HMPJrpEZv16rOWLF8GlWgYQ1/kENUXCNbvRYPFyN8tNSb0S95sgdESXnlquLLhxfI0hzJcOESI+EC4MS5uygj8i3lmoN4TLjEOtTt0vDOBPOrE0zzFBfjZ0gGGglt72/bXWHmfbUN0yL4nftGo6r3H596LdKg3KKbvMrwAo7wc28HqEYp9Z91/v2bj33dUXcAIeS7AfzLAN4BUAD4JQA/vcH1+EzHZJHhsoghFMF7yRJ30nVFyKhEluQ9SS6LUrUorygp3EzRtsqANaDBLiy2539b604DLTRXjhGFiUm+Y+4hY30Ua0glmpagEJpfGFKBoWmJboIK9hHqc6HpAYkB4UhBHVgGtQbO0HkKKSkm8wGeLvuzkXyeInsyRvz4CegqR3u5Qj2554AF9v3aqvKd4QTjs2eo8hhBaeyCjHrQZuXAqMSX7jw2dJGum/huQIo9nq0O7TymaSl8T2IYFj0h6l2VsdUirRsf06sTALrV/fjRBa4WQ7xHr7ZesxnWlqhouLPvsQhZm8y12oz+E3p2NqqTYkgVMr/RTga8xrIKsaA+fMLgewr3kyXujZ5hPFzfq3ZmepvKTtPSnQkspBIXoUDTMoz8NRJ210btYvwMQlJcz4dY1oEGJ5lr2u4aLxyIKNGC5NPp0CXFgDWOZG8X+ul8gLrmGFfcdXe6G5BF42PREExrLVIRsMZdi64NU934zod0E1W9GYS2UM/5frpBBysMzp/h3VWEwXi2/f0jgD+/+ezJrULxFyc3t7akXybaqsHq47cGVPNCcTAhEkL+IIA/DuAjAD8H4NegGem/A8CfJIT8EoA/rZT6Z0be4Dws8c5wgjvn15CCOS6VTYZdkr6dKUZx4dpBwNp8F9DzQ6nIC7kATBsPjQoRei2uSn2shK3lxShRgFFWiZhEITRClZWRqYCAwGiLilZLQOXCdzD2zbhepQd3mKsiwuPpCa6LWHMgiULTUm37s0yRXJ6BjxfwFguUH58jvxn1kputUkfJEmen672SLAKH2rMJtAtgsvHl06d4uhxgYTz+mEfxPE2MkAqMohxZvHtU0BNgNjOvvIgwnQ8wX2pH9ps8O+qzlJJikSdY1YFzstj0dLShQTKasmNHo74HpL62KbMt5LtliGkdgBKF+0m+lQxfVURM4p1ofa5CEePLqK/PMChxns1w591LtGaDdTUbOS5eK1hPHu1QUCYRxAVYULsk2k2K4Y5Z4KoMsXrwTq9jsxmN0YO1GzDqtT1KzrFBuNiaGz5vsie8hT/IdydDJkCZvg67hB9CKvDl+5/g4jd8jLZhuPnkHdxMRls/9/7FJU7ff+Rmo68r3qKW6QvFbatNAuBfUkrt3JYQQv55AF8C8M9MQhwGJUaDOWLT1mgqXbVYwWT7oHvm4bdkXBZoBYxWMO2dZnRGAZ0UgRdzA8gFQQ7qQBQxU07Rw/ck0OrExIhy5qDXZQBGFN7L1g+gbD1MDQrWWi3ZVl4XcXmz0hViZNzubSzyBN+anOFBnqCUnhFjlq5yCFmDZDJE8Mld8GyF1fW4Bzu3ldogWmGQLnsghHoZO7EEPYuRzrrJbjoYlYjDEncABGVsyOS6WjzWHivyawQGndg1VAZ0AnO+jbw2ItnCvfdFEbvPMTTi7ruCMulkuPIq1O1E2bfw8j0FqRQoIevq0PBXaUsAaC5qygQG0QrxYAmPCcSTITK/xtL3MQ71dbQygvb6WYWVF42uKo6tCLVRsP7341WMYVBiPJwjOtfzLI/KtQVRstpKhpvC054vAKPhGSQr8GzlxLJtXN+c9JDem8/Ok/kI35qc4cPTpzs3BbKFcw1JudYwtWjXTfeJfUF4C1Xsvrfs9T6GCE+ogucLBMnKUDWOB3Z9+f4nuPfPfQPRdy2ARoCPF0g+fgeXDy/cezgfTpGdTBHdv4KXHse9fZF4y0A1LxQHE6JS6iu3fP+fvNrTeTXRtgTLRWoURSJMp0PcrNJbffZ2hV2wW+nB8+2Dwtys0FZFFvUZBZVDkGFjJiCUbntaOyIAuAgFQipxWXBcVcct/B9mS9xNFj1vNOucbv+knZ9nVCLdMVtcv0e9gFjof1lrZRWfeGDe2r+vqjmqPHYgFCta7H7PnsRlVTY8KhGO9eI2e3jXJMTdn4k06iYAnBRYZc7BtUrNZ2DReuMdIItu2FlwZRItlVqAeV7EeJxn8I2T/flQezY2FXe6lmIVOmm/Io8cmKbZ8LO0qMkQuooJqXZ5sBqqzANkQ1AeSb7vV9T9+6moAiwXqVaNERR5EWFZa1PoEEAJ/Xlbc9190RVB0Ne5f5zofNKr8Nx77bQiN5Ok5XFa3p77XXfWoJGnV2du5twFcD2cj/EgT9C0GgzGqES8wT9tjGtIKRloo5xXJACH8pSSYmlGBDxbQeYhSLTU80NJoAoGmYeQJYcsAoiSbyW0NQ1LojUqN7LkIEUNOlsCPkNbUP36A2L1zxMelU7OsW58UCqPrshf6ricI/zce6/9OG8iCCHff+j7Sqm/uuv/j50hfgjgBwF80H2NUur3Hn+Kby5a5WGyyDBZZKiEj8d5hgd5jPSAVuXW75Ba+i0vIgcaECV6qvWV8B21gnotAn/tgRaKxiwsa3eBx0VoNCx1XIQCH2Zz3f6kKYAI5R5Xd0AvthdR4Tz59oUwycImP2ut0wWY2Nfb+QajevZmB/vX5XPw9A6ci60ePCYQjBfgpzohJqsQ8uoEqPvHsVzKQjDtVk70nA2AFkk30bTWgYT1/m/veVDZqwptUlystMRaKSkyv8ZpssBwPNMizXmM/JFGDrqkzgQobbfANDasObEgGkATUu1Ab62pAKAQ1AGH9kXTUm0WHNRbidDGsg6xvFnbm10uh+57pWRuQ/YquGv+IIcSFPWiT3C312RLUNsXbqEgG1WWTYp1xZ22rWw9rDqIy+bAc9CNxlh5WXcYOy+2ogDzZepEtYfvPtHUC8NDlCVHcTVGtYpQlwGk9BwgphtlHvd0jtuGQRYBeLEA4QLFg7soZ7t5oNIgTf2gRtuwrUp5MzxfgAW6g3Gstdqrirausfr4wRs73muO39r59x8A8BMbX794QgTwNwD89wD+N2iM3Gc6WqV5dJVg+DRPsTwSPdaNugywyBPMi9gZAVOp9S2t1qclylqHcssVC3ndk1HblQzvxzXeS5Z4ZzgB9xu3uH5tEbvWqA3rMWd/3lJErFJMfUAswGp9WpUW56JO+4nMMwmzK6JdHNn6sQmCeuZvC0/vJMPozmQNVQcQ5hPnrLFZJVq0JTMOBt32nk2KhVE6saAkAEjY7laZRQjzsDJVLXOqPLZzwIjCMCxwOp46s+BinmHxbOSswGzbnIcV2HI7GVqpPea1KKGTYNlqsWxG1rZT9v/3CV27mV7NwSvurue+uMmznQhFaz68T2JwH8K0+/80rEGTEiTSGwk+C1FPMpcYb6tcNudmloIT3ZlgUAWoa+4S4XqTQw9SZzY9G6UxfM6rEFm00i7zxoTYzoY/ffAOPCqRQSdoWQQoZxkmVydY5Iluf1chPrxzifHZev5d5jGeXJ8iDkucdv5fVNyhaevVbl1Tu3me5wmiqDxKEINw4e4xa779JkN9G2UDX2Uopf64/Tch5Hs2vv4d+153bKYolVL/zUuc3xsN2RJcFzGWjY/rUsPfKQHS5xC6KIoQN3mGWRUi5pVxqq777dJbdt72gb4uA0xrD4tGe/JZFOlmRXMWFlgKhvOwxDAoEbLG8eV8r8UwLPba7wDAeVi45PAyVYF1cAc6FY9FdZo/UlDXzuNhhYHUO2s79+omQ8AIJUsPShxeQEWrW76FoMg2FlNbNdrv58Jzs7nUb/a+Z40U1guxBUvVjY9ZGaNpqUb0+jWyaNVRWFlr2U6uTpAOluBx0YPmH6pI7dw3Y9K0StdJSVN+NIrYXlMArrV5HpbOFquuuabB7EiKQlJMO7PWlAnXXrbXwvdaJ8rdPTcL0rJBiULC9Jw6DUp3HZT0tDG0vX5GyaXL3bQbBX+Qg4Y1mkW8JZS9K3hcIM2WGJT6WXPzWEXcZ7qZFEMqMeKt22TYaFqqBTI2nOrrxscnM40iZg8k3jdUrGKeYTYZ4mo2wvUqxXUZYtFoDusXAYzPnqHMY9xMRni6HCAw7izdpGhpJftEt+uKG63bGPGyRDJcgAudFKX0Xqkf5ysJRV77DJEQ8r0Afhja5eC/U0r90Gs+3r8I4DcQQt5XSn1CCDmBtSraEccmxB8mhPwZAP8HAFfzK6V+/qXO9jWF3eWEVOIsbPC44JBKVzxWMHcz9KzAWMJYaPtL7M72AWx8T+uU2oc5r0JnEgoA7yVLJ/RrgTtBFYDVIZKg3EmzyA3SFQDGQYlJFWJSadm2NCghW+9W5KQFxVjvvoXQXDh9zi1KyfB4qheWLrHeRmQEn6Ok2EqGAFBNMihBwWtdbZVPdavqWKspAC4x7wtdYeg5I+t8hnXja1HqYo0snRexm5VZqkbI6977okyChxXqeu1rR5nEzfUJrufDW+8P5rUIzd+bIu22WvxkdgL5NQ+B3+Cbz3R79izKe7SRuuaumrdUj80QhvbR1b611f6sYzHUVS4CtrsAZ2HpVGCUQQLLIgAmOhlagNSmPZolq3uRBOEC1ZPxwWsD6OoxTApkq9xxgbsAJXuOdeM7uT3fa3duMm6LwlSQ+SwDDzmW8xTzPMGTPMMnywTThjoayq6oBNPawXnh7nUbbWdTA6wpTTYZAsDj6YkeS1QcRR7hyc2pA3F9VkLh9bpdEEIotJ7ovwrgAYCvEkL+llLqV17Dse4B+I8B/DvQhPy/QQipAHwRwH++73XHJsTvBPBvA/gerFumynz9mY6UCZwHHq4qhlzoecNeIIeg8Ci91dboeYJ5rdvl+h6QMIXMF2v7qM5DwahEyhokQekg51J6jvS+yaezi30pfCcSDOjd/nUZYVoH+nfy25FyQlLMyghXZeg4kVYcwJ5/KRkeTE7Rth7unF/vTIpO1WNHK607fyrmKery1YAQumG99Lr6l7My0iLflRb5XtWB24BYUnwSlBq8sGPjoBViNFdTSorJMnthQW3fuLtb7c5ZFeLhYg2zTzot7t77MvdjWfOd1kh65rZOiMOgxDBcud9jqTQAnGi8rWJtIk2ZQObXzsqsWw2LkqOYZ300qZ0/xyX8bAVv2IJEHB5vwOsF6sl+fVViSPEsqJAOlhjkCZZ1AGFayV0ebtX48Doaq+Etm7tdIYwf52KZIKh9LPIED+djfLRI93Jdu2E3Wyszj9xMisAa+FQUISaLbMvQ+nJygtJo/14uh9onVNKdThvfjvACH/EH91/ul/zfB7/72wB8XSn1TQAghPwEgO8D8MoTIjRN8McAfIdS6ikh5McBfBeAx0qph/tedGxC/H0APq+U2hb0+wyG2hg9MK+FTxQWQu+idyU7jUgVbqfXHtESPTbOw0rPcxTBeVC7ZGgNf5dVqA1ODVJVz+Gkc1cITAuIGfi7tWOybbTNxdN+Pa2NDyBrHF9xV0hJsaoDXVmaZHgvKo1lVT9KyfBopnf/p+Opm9UcG+Vk4MyJ98XIJHbrEed7Ess6QNMephxYeyErur2sQ8yqEJMqQNpwjHjlKscX+WzzIkJRBb1kdNvv2axinK9mB5DzeBWjlB7u75gxcV67WfFt82IABhykZ9JxqDsKeRXC91p3P3TPiXktmCLOKzLlFbIk7yW+/GaEfJ669q2+//pG2oQLrQDDfaBoNL+v+77Deq8SjEfXsoKlpFu2Z6Ld/b414Or2JSlh6zn0YqWd7B/NdDL8dKU9KDP/du1c6zBjI+raYBkK0aqIMFlmuF6lTgmr+3orM/hZjLZqkH+0N1ccG2eEkH/c+fpHlFI/Yv79LoAu8/8BgN/+sgfcE18yHogAAJO7/vGBnwdwfEL8RQAjAE9f7Nw+W9Gd22xauDwPh+jY0OjQci9x+7agBoYNaJmsrh8dDysUeeRcL7qxbBguYbz6DCXkUNjzuxeV+OJIz0quV2sCh21nUaK0XNoOArODqe+oEK1ljk2G0tAenPdb67mK+V6y6Nk2Ua/tURxsImoU6Q0Eupsdi1a1i6xUBNj4vJd14I7teW2PCN5N2hbx1537OB6foQDgiNb0ZmiUpIdHBUUpE9xPKNKgRABdJfKg3tJs1eLrAa7KCJOKbwFQKNE0BMvHDNj2LA5Yt6At4CdhjW7V87UIfNswM3fV50DN9Wsqjlbqipo3TM8Zsf3eCZOmeizhAWhn4dESad3YrIiZ12IclBiGhbseUhE8XQ56VmTWBcRuQDTYztfiD62HUmpXkVISSEZwVUYYznTFXtYcN3mGqzLSG0upFYImywyrMsSg08q2Ye8lAO5+s/fEB3cuMRjPMJqnaB6/i2Ud4Nce6YrsXXwDALC4PFtTbewmKE8QPRuBP1mBzve5Pr6aeAUt02ul1Hfv+d6uX/58HnrHR0gI+csAfrc5xk8D+I+UUteHXnTstb0L4FcJIV9Ff4b4maRdHAq3oL7hNgXzWsC4h79IULM4dkWLmek4RoMFeFBjuUh7/mnAmj7hexJ3vN0VZTdsMnz/4hIAkExGeDQbu2R07PnvUjOxYBxAV+S1QXraRd62MU+Thatu7Hv3vNaIaJsFWRENllJA6O0B05hEZa22mg2HdhtaaYa55AugB665LUTrYVL7GO9RVTk2rioPjYqQ+RneNzJpvqFdWP6l9RJ8vEpwtelF2FlbqEnu1AiD76KI2NB+iLVW+UlyUCZ2to43w6oNiZKjbRiU2GZX0rAGPSlAhhxoBIASeMGk2I3MbzAMC2TRylETdBeBakeaTrvSaoBOiwSVYKjAsGx8lJKgaXVCBMw9JSmemCRaCh8PlhkmFXddHhuV8HE1G2GcLtxm9VB8cOcSdz/3EOGdCbKlbr1//fG7mNYBfvHTD1DVvAfYsVE3PlZ1AO/mxKn+7HZ2fPlQeO0o0wcAukTH+wAevaZjfQW6Ivx3AfwD8/VfAvD7D73o2Lvyz7zUqb3hIHs+U+s80LZej/AMrBdA58JgZmcHvfjM97oKKXruJ8FrrgEtO+Djt7kxbB2HtpDSW4NVzPwFWMPaY8EgBUNZc620woRWRiGa4L5oOIbCxyA5LP004hptaakGRRFiWBVukXDndADQ0N1s7KoULRx9VYaYlbFzZrfgljgst4jYIa8RskYjM1sPsgUWDUFIAcn2P8Si9VB2ULP7ojTJxm4oNtVsdkXKKzSl0QFtNUAlpHLrobJI3W4L2IaeKbaImWc0Ogk+WqRa0s3M8igTbh5aCYarMtKISPMRUK9/LNuqswL39l7eBPbYnwdMVWkQqseos9iQQkvy7dMCpUmp3SW43pgRWu+USzs7fYa60ZXbMcpEeg6rEaVCUgRNo8UpHD0ndtfbClikbYlZGTmpvU1fSvsZWo/GQjBMKo6bmsL3uEPw2lhWIWTrYZQst4yruzEMV9oN5HQGdraE5wukgyVG09y55vzCw/fxwTLDu+dP161Y85kv60Bv1ozo/PmtV+fFwuP85WeIf+/gd78K4EuG1/4Qmg/4b73cAffGdyil/gAAEEKIUurvE0L+3G0vuk3LlCgde0el9mee/3xfX3imBVRiW/bLcrxsWJUP+2+7Mw55raHvZr6nZ4wSUnraH9BUE+EGCMZqFw6gaQiWDN81ALVxyNR1V3SpDofC8gkLYxmV+TXupPNbk+GhsChFtjH/6sauqmKf7qWUetZnqQaWezbc+kn9s2XNtdegYGhaXR2W5nCHXDGEUWCZ1lpIfbRnDHkvWTjnBAvKiWUJbnb/Zc1RNRqgAwCn8RJRUPU2BoWgzivTRuo3ut1GhaNSbLb/Ur9BIihWgiAXwD+dUIT0HAFrXBX9ZD7SAu1mlvZ+mmPR+HhsPBGt3F9ItYvEPquvXdenmyilpJhNhkjFurW9nKeYzAeIN5xWdJWvBbQ3Kz7dQoVThyGsgBJKq7rkmssoSq18BGg3lc997lNwv8FHN3ewbOxn7bkNZNdQ+FDYNrnvtT3jYetCM6kCXFUMzyoC0aJXJdprsrCep62HlSCY1hSpz5FyO5/WMoljRVxiPkbEuxu2/c68FvezWS8ZAuv2aykZUtMGtqCe1xWvcyFXSglCyB8D8LehaRf/g1Lql1/T4XoLJSHkKAme2yrEv0MI+V8B/M2ugDchhEMLfH8/gL8Djeb5zIRndsld+a/bYtOSyM5e3DxAej3vPwBIeek0QTlvHPHbxgBL96BQ1/5bL1TcbzDN01uNhLuhk6IHgO0UCwZ09eZ70s1YzodTB87Y+tlbSN/HxjEttueJ7nXOiwiLIsasCl9Ifg8AriqKRunk0QULhVS4Fq2d09lYlaEzeS2qwOnAAtobM82WCBf6d0lFcFWGKAQF84irCq290zBcuWMUVYDKyL5Rs3kb8wal5CglgVAK/+jaB/AOzmalk+Oz6MqLaIUP71xqP8Rn5/j1eQbfM1ZSftPbgO0Ki+Q89GwsFymwgENFAnqB7qYjO0Ouy0AjJev1cto2TMvdNfY+LaFqD3Ieo55kyG+2Bawpk3j/w0/AqMTH13cwqULdjjZzZcu3PAZluouSYTden64CfJoTTJsWoXfYjs3GQni4LgN37Be9DwH0Ng8RE3g3m+Lu6c2WSs6LGAa8TLR1g+XLg2oOhlLqpwD81Gs9iI6fJYT8FqXULwI4hU7Cf+i2F92WEL8Xugf746bMnUKTGik0J/HPfRb1TAlatxsHtLnuoegmQ4+uE6IFcwB6gd6U0LJSbd1kaL3pLAqPdxw0usfqRrPYVRftjlZSiCrQ3oUHwGrUa3EaL3E2mmJ8/gwsqCCqwDmQ9372kPQa1cjXkEosn2Nz8Tzhe3JvG9klwzJyxHxp5oLPG9q9fp0UbTIcJDnSbOkqHgv0AXS1KFsPN6sUn+YpQipxPzXJMCkgBUNWr4yuqQeAY1L7OA9qlwxTXiIOS2Rp3gMiFcV6gxJSiYy1KH2KZUNQti3+0bWPe/H6Qz4PJD7Mlnjv9Ap33r1EtYpMhe05BKlFKu8LsQH8sWpAaVBuiT5Y6UNA309t621RlioDLOlViLWCkh7qVQQWVKgnGVjDoKSH6maIfKLvd/vM8bgAi0uwVFdHFwZMUl7fddXetPYR0tYl/ZRXiENdwYsjqyY9e43w0YLgum7QQIuLh/T2yVHT2qQYbiF1XybuJgucjaYYnev54dYox45uXtHxDoVSr5eH+CZDKfVHO19+RxdxeihuuxP+OoD/UCn1FwkhPoAzAIVSavqC5/nGgnkSoyhHZNpsyyNu+l3Rg9jL7QXBfc84OdgbWs9+dnOVur/PViS2lUrb9cLTdYPYrORawdA27d4qcX0M6zLBOvBwujcJbu6WOW+QxSssa03+L8ymQLpzXD/ADpko14LI3eQoxX66heXmbYoPJFHhlEesIXPXaxDQCEErv0aJcov4ouGOeO4bL8KMaUj/svF1F8GBrNZWYM7bsfEhWt22uiojPC44fKJwHhaoK64BL0b1phIMvuHy2VnipmRa9/7ZpVJCjaNJxAiESYofLQFGtCaq7x3ekAjTEjxGAcVWh+NA26F126HCcC3tvC1hDSK/dtQPuzG07VxGJUTJoSQBqRsoSSCLAE3FUeYRwioAW2hR+MWzESazAZKoQDpY9vRB7RxSin6lfhaW+I0na3BgwBqcjScYnMzA40JzWh+8A1ZLoENpaDaerUkV4rJkuK4bLJQGpvnwIDZmsbtmrVLpP9OaIqSeS86LhiMVPkojBlFUgRPat5s82eru0vMCidabUfFajYG78ba4XRiFmj8BYA7gTxNCCHRi/Oqh1932Cf0YgL9NoMrQvQAAIABJREFUCPkxAP+lUurxKzjXNxoxr3AWFpjWHL73fG2O21wCNqOV66TgUQmergxs3ThmCOaQeVo8nG/RBEroZO4Vm1Vc637vvmPbBNtN4mXNnTixBbOIAwlxMygTCHiNoXOSCB1JOe+co1voOwnFtpKPQWwGTGAYrrZmX7r61q2kWRlhaSo3mwwndWvmTBwJ85Ex7n7mqgwwbQwZ3VMYcdlrl5aSabK1AdIcxfNrNTT/ejJGXXPM88SZKds53thwTSlRzszXqq10xRScj2Ln89LeiZpovxTHT3Rs4mcdAvuusNJ3ttK6P77BeDhHXfu3qgZVwgfKdSvPIoSF6VqI6xQMS4jrFPUi1veb0asF4KhBlmfrUelcMf4/9t401rY0PQ96vmGNe609nOHec2/d6q6yy3F3JCyCo/xIQEBAAoWEUZYDyDgRKAiJQYIwRJGiCIEQEQoJRAIsJcgkAitArBjyAwkFy1EGGcdObEg73e2u6qpbdYcz7GmtvaZv4Mc37LX32cM5d+jhhlcq3VPn7GHttdf63u9932dwgtmEy405+fv5HF/9oW9icDpDW6R+QzU4nSGcLMGGK0TTDIvpCIsqXSdBRQEmDapUrAX2u61Cq9uSZt7WEXZh5tXEafUDoL3zOca4ThFQ6b08+99pzAQeNuG9CfiMSURBh0S035EKkUYBBh++93ov8tfezLG8gfhpAP8RDPfxv4JBl/4JAL/j0JOO2T/9eULIXwLwRwD8EiHkz6In7q21/uOvedBvJQgxfn9OyFoqiqxKEdC779CkHWjfRcJN2pmeA+hse8C5m111yvK3GKoy8aoj/nU0gRQcAQ38HMhxyRxi8Ngxb7upK0VRVbGvThx8n+9JirvalozJDUCOg7f3k4dS1FdUtV0kx02JJGoQhy2SpN6ZGJ06D7c2P26xDcMO8WCFaLACj03beVUbSTq5tTYUQuHTgiILCE4igqVdeJydUc4VxmGHSdT4z+dEEaouvPNiExCNWlIUXYCXxdCr4Nz6TERvVIfSnhtq244uiWzPuAOqEZC1j2JMKWp194VQKHNsVRcevGaFJki4wJcnV3j0+DkGpzOojqNaZKjLFMtisPN5zqrJbSDKZr2R6JoQokhAAwFRJBtap1IyfH51jtQKIwBmg9c3zXUzRxoIrxT1pdENftNv/rsY//ZPoR89RLZ4BvV8BXGVgQ1XYBcMSAeg1wucv3yGZTnAszL31CBXMdc2IW4jS1siwDSF0BoA2etA4sTEXVLsGIFZBikgKYrOoFF3JdNOETyvUrxfJa9ENXGi+cDbnymqpsPyLc8Qv4PxUmv9swBACPnXtdaKEHJUK+8u31AHoAQQAcjxfeB24RwnXjcau4t37cFjVZVrmx6Lto6wqpI7Wbs4/uGx8K3LHa9Z9yrRvpcgk8yYHQO4mNygUwxZeFsv9dZ7WaSu4w+6JOjsmCph3CrORYBRW+N0sISQDGlS9VR2JM7GM0Tl4E4yeZQZ5ZWLQYFZG6JWBKUgEBoQSqEQEoVkqCVFHhg6Rsz2V1gxExjF65Z20603IY8evUBbR5jOh36G+NH4Bh8ODQGcEQ3OpF9o90VApSHZW21Qt4FwyEHXfuZUIaEKtWRg9+xicCZte9bJ6zHUkh+8tjjRGFj/wPvOe130K3llW4JiFYNw6b0jAeM28XR6io+XQ4zDFheDAuf5fKeoA2Ak4qTg3p1l21bqvuGcMIB1m7iDQksEKlIh1IeXwJgqxMy0rGvpkiIAUHTaba6AlSDeCNqI9yssBTMG4CLGh4sRntQ9oQdhNtyHNmN9kM2xzsWbinelZQrgLxNC/iiA/x6AJoT8YwD2c2NsHKNd/JMA/jiAnwPwD2itj/uXfA8EYxJJUqOq7iae26dd3PqbJsZrre58u6t8w6K8nsbg5ojWs7CfQLaP18/jrAHq9OoEl/PbyL3tcMkw3OKchWGL83zuk2FdmsqnbcJbrd1XCWFbxNuz0PFwgUWRQVmlmH1BuUCSmOR6VqWYtQOwLcIpJ86d/nAyDKj0c7O6DbG0wtIuGY7ee2G8674QmM3WIt6T0QKMm99ta6VuByPa6Kfy22T9fhve0WIY0ViyAAHhG/ZGQpvPtfe8WG3UmEmPfHTXk/tuPeXHUiwciKZPKFeSeg/QVW2skJbWZcXNlRlVnmLkwp27+4RSFLPFEFIyDG1HpR+MC+/ZeWxGvis40RBwLVIKRqyYg7rdMj0WBr2rETNzDuoj+4eAGPcVIxdJARBcNhT/4288xmfLEX747/ww5nWKTxYGWPRkUKCsEmSrZOdm2iXFq+nklujGmw6Ndyoh/oT991+B8cr+NwD8/mNPOlYh/mEAP/YWuSJvJVggMHpwhXCRe8fsu8SuCq9T1HutNV2wgbp70+EURQZRbeZvW+jUXdE1IWazEabFcU6ja/+u2sgkXNZD1QqFQVJ541w393mTvCdhF1wASNLKS9A5lZ16By2kH26hPEtWmLUhbtrbnDQzgzOtx138xJgJPBzO8OD8CvGgQteEGFie3YPzK4zee4HkB19CSwIaCN8GP/nyF0h+8CUQEjz4LMPik8eYXp74OWo/KXKbpFKLhPSffWuXb8QTjH4oAOQiwPSAxut2ULZuM8e9pLf9mH5kQWcJ690tMXMpGMoqwbxOUYrAAo8MqAMwYJY0rpGk1RpA9RoGtstyACkZRpIhGS797xmXXreXBgIIXl3VRigK2dMOfpUwSc60VQ8+jmowCk+3EYqC9YTD/8rLAb6x/MBv1mKqUArDeW3r6BZty8VgWBhR+cXwLeugknfJD/EHXuV5x2aI/9CrHc53OQiMmkuP//fhcAahKD4czvD44oV/6F0k3Nwcoo/ycyLcYdhieDI/KFbd3+XuSmwuuXaK9Ux2j7ey6jLFdD688y7dza+KNvLOGIBDgwow66yxTQD2mphha2yUevzMXcGpwnlc4ywpkYaNmVkCEAC4ZMbfr2cRFQiGsGmPJkQAXgxhHLY4jwOUggKg6BRwHhtqQmbPt5Ot60cSmM8QRK2XwAvDzv+OJQ1ISgGhwZIGQdQiSiWih1OQR0PoMASvrhFPl0jKxGuc9vU0O0tt6M9EuW3j122IEdYVURbW/vuP22hjDiV6ehcBBS5igYeD5S0VH3fOM/s6+1SQxmGLcdgg2SGIrQT3m6BGcE93ANYVrTeW9l2FDqmsD1YunElEXHjx8ICuhbyVopgucwjJcApsJkUmcfbkOcgrtHT758Icv7lWs0DgJKI4b0PUbQeJu28+TJWo/Vw6Ztrbo0lNMA6AmCmMw7V4fy2ZnUua53y26vB/1d/GR+rL+MowwEloRARWbeTn/H2aVn+tmJzfgDGJF9en9z4fdw0WBcheF1TzN97MsbxuEEL+4UN/3yc28zZ1Yr8nQlqQCaMKXzm5wodPnm7wAoHjSTGg0jvPu7kZYNRshidzDC6u0C0Gnl+1K2hgnLDbIr2XTmb/GGezkU9Obsfo/NZiC0zpB3PizTDJyGlhdoodTGj94EwiGxaeUpKkFYZNgU9fXHgbommd+kTg4qEV594OIdmtZciJRyuLDDx6TFQiD1qcRwFqGfkd/DjsfAVkIruVFPeR1t3vyI6ZVbNK0LyYIMlMhSiuMtTz3KgXMWmqbdmgUwaVvE/RNAxbL5DgNh6uNQgArMx38ttiRvBBpvBhvsAkWx6cKTunkBeLMaj9rM9mJ2bR7iVDqYwzQ2I5qc6bcpBUOG0K1JJ7/u6+yopxYXiAkuHl5Rmk4AinDdo6MlKDbK3mZBR7xIa11cqCsJ7NTrBcpZjkS78RPL24BE9r48n4uQatnkPOKdqXJ+gWA0SSAihAR0uoG47iZuxdTpz9FbDpBcmJxkUssBIBapXic3n/+3AcSC+C4L4rJ8IQWGSxa1mbmXAARgwa+iUWuJbfBmMB+PI9/MjECAV0yoLhqsQ4ijTtLZI+AEMzuQOe4FVDNh0W33pb0qLf8fj3DvyNYI9R1TudEN1CCwCDqMbpZHZr93VMCs3JPw2i2i8wTRcgCjpkeYHBxRWiJzcIqwVoIHYqcJCe9iiPWt8Sus+g/JuffglVF3qQhlQUs8qgAd0i12/j9hOeS4oO3XifyPICyXC5AW5QFrH6+dU5ijbCVR1vQPkfDZY4H818m/AoSte26l4sxng0vrkTmo7ZWdj76boN6GZ2UY9gXokRansujunH7mtLt3WE5fMzUz0yhdWzU3RN6IEh3AJ+pG2vb6uYMCa9eIP5f+UTY9ib42VRjbhKbZVoKxFK8DjV+DAr8Xg0NRui3rVr5pjmNh6HDR4OzXm/LnNv6Oz4cP3KUCiGVR0jssbHTqyBMYk8XWFQDTAlx2XSGFOIwhZtF+B6OvZkeXM+xYawgyOY96tmc50GKNoYxXWMDx4YUfnRV74N9kCi/XiA5sUE5FpCVpGnXogmRFxFYNMGzfUI1SpBI0yLdxKvvAxfQCWmdeo9H7kmeJyYeWtdDrDE3ezLAqKRBQIZF0i46Hmcmgo4C2t/Xp1EXB50tgW+ufmsUWChWlzWKS7iTRS7S4yAaZX2r8kwrRCPlnhbYYj5b+3lv6PxqsYT72xCVFZqrW5DTLIl8qxcL0i2XbfL+dvFcDI3KhxUYZIt12AT+ztKFeJBZaxtcgLNOoST5QbvsJ8InT8cT2vQPXNNpzKynRA+fvoEv3Z9jk4RPEorD8I4FG7h6ZPMATun1GQjaewKYauFIGrBYzPLIT2YfLbIgatzPFsN8LwK0WmCnFNkQYc0bJAka6K3U3/ZF20TYlZmeF6lSIIWZ2x+9LMlQYta8g01mCgwrU8HFsmqFJOoxdOyZ8J8T4Kzuz6UZCi/OAflAtUi39lVYHbztOxJ8fWTYb8z0daRaR0z5TsGEe+suAAw4AAjBHkAfJSv8MHoxqBCt64NZRffgCqvNhMGHeZ1utbbjGojQM+7TfsqRbEs1xSLPmc0Cdqj8m79zwiYa+yL+QRPJtcYZmYxZ0x6KUHXQnYdDtc6Dag0LhVlhvjmFB998G2w9wj02UME4jnE1xJUlxOsFpkXUAijFp0VR1gtMpRVYlHSzcbs1tEW5nUCoWIrfq5wHlPU0vAqDSp5tz0W1wRMEeSBQGbn1/1wWsau0q+typETYs8DgTxgiClFoiNE1Nz7EgrLTmMpDCrYhZP2a7sAjCkMRkvwqPE0rl0djDcZ78oMkRDyk7t+r7X+aULI79Fa/2+7/v5OJkQtKZoyRbHMPCH6vhFELR5/6XPkN+OdaNVdYAI2qBEMS5TPz/zveFbdQsq5nb0LV80xqpBEjW2Lmgt/NhvhRZnjsuF2fpFAxNQDMvYlxn4rrulVosYxQm7C5uVawaZuQ49E3Vbw12KtTDOfjnBVmWS4FNSi93YvntmwQAbg+spULKsqwapKfPv30xcX+LXrc9SSIma5AW8ccA8ATPLIwgbzJkbRRmanvpXkmXWFZ6+I+yBMgUetBzt0TQg4L0d7vqTczVMt2gixpTYAzk3FApZsS3E7uJ2zucU5D4BHSYuPxjeI7PfJ7Gy8H/tamrPWiBQkXCDrVULuenObg7JK1vxPGAWavt1X5xRw7OftqxL143qV4bJOEC1Hhnu65SovpBF0Dx1wy16XEe/wbJ7jeRUj4Tk+2vFZVosMlW3r+s9h/TWdco5rVfbv9zhsTSK2pPm+H+b7Aw0gwEWi93IQHVgpZhLjsPEUIjeqOBQRFziLaysWHqGQKa70BYZqhHOe4jwmCIgxbnZz2Hmdeqm/qorBuEAWNQb1nNzPjPu+waIA+evOEH/xzRzLG4gf3fP7nwbwVQB/7yREKRkW0xGmy/y10KDJ+RSDx5covzjHy88v1q9vwSkbQJrQLEo0EOBRg6ZM0ZQphk9ebCTEPjm3v5C6Vt+rqOZvRxrXPtnsSop9grRTsOmb9R6L51//AN988QgfL7ODThOMC6TDAmG23lXPp2saQ9OG+PUXj/Gr0yGkBgZcY9aGuF5lSKIGYdTeguT3IwuNWHYpAlxVA0S8M23tHmfUwN/djOee1aGd+95KQL3ztS9mbQipc0hFMUxWkBZMdCxiJpBziZgSTKIWT7Kl/1yve130w1kKMakQ2lkz2hBSMqtAY3RjjQwd8wm+L93Wlxa8S9SSo2hjsJ6aThSY13Qt1bcRnElM8iWKNkYt842k+DjdnwxduFHA6WCJ8XABKY3p7+IOYLaASpzHNYqOoxQMN+U5hjTEeURxkZi16aqO8Pli4tHGZ+MZGJNo2xDVKjEeqPHbmx26kE2H+bszQ/wv+oYU/dBa/7F9T3o3E2LHcDVbz/Lc4r8t4L0v4skC0cMp2AMJBBx8+G3wqMXV0wvD0WpjMx+rI5vg7M6NKVNVxC2a0sw6yudnGMZfgIU9GxrBdibDXS2x+4ZrGfbbX45usa144Xb7zqx3uUohFNsJhnHx/Osf4DeevYevz0foNLnlKeeCcYHsZIZoskSQm6TmNgOuUvyVz7+EX1+sFxXnR3dZJ8jKDMOssIontxcDt6hmUY3SCms/L0Z4j069KIOrgO6y0O7qIpDQJMS1YHtPAMHOR7cFsfux7AJ0KvfVVSqZ36jsCudbmAUGRetawX206qsAsvrhQFCzNjQeiL3v2l0nTWcEKYS1XnJuG7WlCBjVox3OKdZzcd/Gw3B6bTVv35dSk5ADKpHw10uK25tf165252xUDjBvYlzV69mopRfe6iL09UwTLnCWFpgMFx7s4kyrqyby2AK3AXWo45ibhMg6jbO4waxLcNnEyBjFebyJUn1aDvAVW4EOJ2ZkkDhFq9KIpAfDV7dvu2t8bxn5vVZ8ixDyFwD8fifsTQj5Fa31bzn0pHcyIRKqMcmXPukkSY38ZIYwWxl37y3Xin0toF0hJPPw+rYN9hqjKmlQoWkTgkcN8o82JZFUr03qZn2UKhyHMZgwyDnuOWL7gjHl24+74PFul1+3oQcE7FPQuPn2Y3x++QCfLEZG+kyZBcUtqU5k2+3+t8+zeT+TSP72i8f4xasU3ygbPAojnMUAC4Dayo89K3Ok0wbJoAKPWqsDyw1AqDcbde91VceGcF6nB30f3QxNSbZxfIwZvqGWFHplF6oytjPhrWRoHeydn6NLil7qzxoSC028rqp/f8msQ8ruHf9aeea2y72jPDBudXLtNcOsWHndA02544iZ7Ol7mmt31oaYNqH3Tux7dRr6SOxVdFyCqiXDvIkRUOnb8f3ELiRDErS46L2WaQ2vQSadomBMorD80X4yjrgBrLjzrKsOZFVCVVb6cPuelcwYZ2/Nct13cCgOdTU4VR4Y5s87VTgfzXyiAizghSkM2sDLDDIuUc5zL30XwVT2Z0xCXj3AsypGTG+/t7MAi3m3IXpAmcRgWPguieo4GHt7laIGoN6RGSKAXwXwCwD+CiHkx7XW38Ad7B7fyYTImESWF55XNRgtkZxPEZ4uIMsY9csJxBZvcNut4jsRDp3ImMSyHPh25SCpjvoUukW3EtxUFVG997EOCQjsToruWByt5K7BKND11uxaAt9cJuDkDIwqfDlqEdsFS3UcojEtoG/dnONr8xTP6y03CA0rdcVRyxQBNZXkZJH59qT7HG7Rq7rQL96Hor/jX9UxwtK8TltHXixgYWkzw45DC4Z6OjzILwVgwRPWNLaJjQP6KxLAuU+EBxR7mLFLYkmDsOM+mV3W0Yb59dNyDdwqugBTKz49bWIUXeArv76Wq0tcRRv1VG+0P3fLLsCyG+OqGnhajdsMrGyScxzVaZF7gMjzYuQ3LFIT7LrCTIVoeIpX1yc4/cYJwpsFqqePcP3p41utWXc9MEnR2la/VNR/nlUdIx8Yu627Gt24xPQkn2OSrRMiZxKTs5tbj48HK8QDeO1i9/0AQFXFCENDn3Bgqs/KDHwHcncStbhIVphky1vdg8RWpEpSdIvBvQXC7xfkXVKqIVrrP0UI+WUAP0cI+fdxTFkB72hCpFwiO5lBNBGkYJ4aQU8C0KoADQTqlxPU83wjAfbdKt76MVK14QYhJcOiHGBZpYjDFlLKO93IszaE0ARPmDzY6gzD9d/2zQkdDP4+c9eAaEi6lsVadgTfWKZI+Akm0wWSYYFwYhaXZpXg+c0pvr7I8VmpsZTmmJy4ct/j8LJhYGSNgHSoQScQ4JB8UhML/LjbjewqiEVh0X5bG6BFj0vaNUZXc5+CSP81izYyhravuaB4/7s9rUPGJXhag8UttDAtWFdJTptwLyrU+SUWgvtzJRRF2dsAGS4pQ9EF/nPs4kUuuwDL2QmeZEvEwnyHDtnpkJazMsOszHx7dtlxz9Xb97ldt6PtAsy+/RjJNMf85dlGMnQIVcAkRfd+LpkDZmMCwMsf3jUSLnGRrHA+mvkEuAuJ7sYuPGq9jyMbmoTIkgaUC4SLHIPJHPGDKdjAXLvvXz1AFkQo7K0YUI2zuMZZsvKyiW4j7JKsA+XJOkRzPUK7vL9U3l2DRQGGP/D49V7kb76ZY3kDoQFAa/3XCCH/CIA/D+CHjz3pnUyI/bhLgnPtGCfTdUyVPmYCteSvPO8bP365MZfyx6EorlYZODXC19sRUOOX53bafSJ8H9beDzc/YVweBEDchReZDgtcnFyjaGLUcgRICtnbdEnTc8GsJfj6Isfo5QWyYYH0vUtoSTGfjvDrN2f42sy4OHRaIiUBYkZwHiucRy0+W0WYtSa55pzi2cosAGdtibPhJh3DWSgdogbkQYsiCIyTvHO6UMYma9VDV7rvUkqGqZ1x+s+1pSCyfQzzJsayC7wT/V3CITDb+nbFkIWNmT0dqhQtDSYMDdo2D4Q30mVEI9+hAeqqPodKFT1/S8C04beti7bjqo7wRWUTTxfgw3zhwSDbbVSXDKdNiFJQBDSwtIfDbf5GBLi+OkG0yEybuNct6W9g3MxTKeoRxv0Oh6uW3fd2CG0eW3Wlh8PZBmVo30aI2vuqr6RDQrWbFsEU4skCHz18hh8pMxQdx1nc4DyucJoWSKL96FEaCEPZqkMj7LE6LtH4qiGbDrPf+L5z+NsX/5r7QWv9ghDyOwH89mNPeucT4n3CtU3L6zFUx5G2lwCA1efnmD574BVGOJOIYVqRx3hBbRuiKVMkPfAISxqMP/wc5RfnaFa3tUKvVxkYVWgs0GXaRFh2BHmgN7QlpSZeleNQdRgPKlAmUVnB7vvOK13wqMXk/AbvtyGkJvj6fIR9XYjLmuLnnz1A0Ub46nSEug3x159+Gb/wIgSgIaytUcwYxiHw1dECk3iFLMjxtXkGqeF1IbeNXveFsNWfM2tthEkOk6jxVVSnGLqWoWhjjOKVr2o2Xqf3PrsWUSnN+8yqAa7qBLM2NJw1on1S5FQBikIA1otT+fkbY9LLlFVlAimNgPq0yC0Yowd0UQyiMUCXKGxvzcwYN5qpWdDZ92W3DG6dtqY7R4xo1L05Z9f7eTsZCvsZII2TyGXDMGuBcei6AqbtWnUhrkuzWJfWlNs9f1cUrTHSdQlsWqeeM7kdzvqsLyCwK6Kg29hExGGLMFrLBIa9TZELp3J0HjdGFi/ebVO2L7rFuovhrK9Es0NjN2kwmszx958/x/UqwyiukCffW14JGncYsn3/REgI+V9hDYIBzGBEvg/G/58Q90S1yDzBfv7y7BYXkTN5pxvHGfQ21yPQQCB+MAX/UAJhAD75GNW3H2L5clOfsJYc16VZHK9XGZYdh9Tu5pUb88JMUSPUbMUCthfweFCBRw0oU2DMwOs/X0zw3tChMQ8T9LeDRw0u3ntuTG4lx8fLDPWeduVKEPzCizGeVwkqwfB/X99OaOOA4jePavzQg2fIsxLplTkX31wMjsLht8OYtXK0XbDBE91Xkbwshni8o6reeE1pbLL6Id1srEpxVUeoJQGjDJPQEOs50RstR6Eopo2xjRomK4Rhh8i2kcObMdomxOV8DNn7Lp2tlj8OxbAsB0jSaqODEURGm3UcNobW0AUbSjkuGfYTYi0Z3PhWKAphj/dQZVhLillHcVnDO8zvik+K3L8XYL4TqYkHsvQraIc67VthbYerzE2nJ0QYdqiq29eRu/bdTFNYmovTrKVWRu4WUIkCZ3GDi0GBPFm9UtfHzfYIl94YeTsIU4jSCg9PrzHJlwgtT9Ml+fINiui/cmhAvTszxDdvEPwuBWEaSGMQACQxIs60ONyPrxY52jryydDdLHfhs9HeQislRbXIkF9cgY5q6PEpdJqBzr+NYFgimOcbih5SGl/B61WGaROjVo74btUzqFvc2MG2mhfQtoo5jAsoRXFVx4hZjslwgTBudrbtdn6mQEBZuH1qxbunTYROh2AECLYQdO6Yf/EqQm35Gc5GR2gKoQPkATCJGqRJhcFoiWEVIy+GGHCzQPTNdnclLkdTEJp4dKZDym4/rh+uzRlx4ee5gIPqtxtmyhFMW9213po2xPNihKdl7EUJUq4REI5xuHuDYdqZId6TDIwLz01lXGJxnaFoYiRBe0tr1SXFWhgUaT4oMdxq6buuRaCJqRB7i1r//BklJGaTEkOnCDglR5PhUjDMWoql/WgJB05ChfO48ZuNUgT4eJnhWwXD40ThItmPhmRE35pXble17rvox772ZV+EYvP5yj7P/JskNUZxhUmY4VllruPYbRgOdFj2xba5sQuHCo1O5362CJg2q5PtSwZGBSrYuvUYF4gGK09XIuHr0WzuEywOMPrBR6/3Ir/yZo7lDcRbMwj+vgutCEQToWvC9U0UEqgHj0Bm16DVNdiiBbfyT4wLVKsErDdjcz6ALvow+U4Z/chqlUAUCfiygG4p5CKFtL1+xs3Cyiw8/C7oVecMsPE7SywHgMuG4qwNMasGG7qUfTkyT0eQEm0d+c9PJUXbhEjjGj80vsHF5AZJWvlF3iEt+zG1nnX7JO4YVZhEjSdw14yilmtuYqcIrmrgqjWLzTjgGPtTagXSmVkgd6FqA2oI9U6/BJV+AAAgAElEQVQDc18YFZHWyJeFjZ8nCWUoGtuQfUMEF/Y9Nj+Xg9ADBmAjqvXunXFz8MtV6k2KnT9ep0wVdAhUI6ygw3ZUTYRS7FdUcubLjGj7fayvU6d6JCTbOU/lvWR46Lj2RacIamlmurW0rhuJxpNBvdHeXHYBloKi6IBlQDHo+M455sbncvzG3r2RcAEHa5JWBKCv3XofIYDtxzIukCcrXCQVio5jKaifp95V7B6A32T2lWP6ko2UKQOmGd6vJcqYRBC1SB5MwXc819BP3h7KVNQdpu/ODPHNGwR/v4ZWFHWZoFolSNLKtDOSACI/BQNAVxXYfIWgNiLBDlov5XpG0R/ehz0pNSd5tuxCLMsBVlcTsKS5BcShzDjdtzbhHgtH9N3eqfbNZGsJPF2FiFmKC2CnjY9bHN1x12XqE5r7TGfDOZKk3rBAYmxddQJrkvOiHGBRDnA2ma7d7p3xLJUY8A5dSJFx6hc35xQeUIBTgBMCTo2TfR44l3ECgBrvwgOLtSN6H9MgdbNVRwVwXEWpKJo9l3nMxEaiDcMOg9ES8Wjp+YdOzq7f0mqEAdDcFdl6KKQwbiydnX9Kax11n2i7wM/tfKv2SLs5sFWaUBSw889jETP46m98B9Wd+0anCJZdgJHlU0YwSc0lAcdrPBbuHth+LOPGgussLUxruUjMJuZIMjSb5rWgB+XCI337oWSIMF8hnCxBDlTI+yKMWuQXVwgeLkCYhqoYYJOsS7hvmxb2DtEu3opB8PdlKMlQLDOs6hhSMpwA0OMhZGbmUyS9Bk1KK7PWbiBRHb9pG33YT2pCmlbPokpRznOEaQWe1re82xiXYIcGLlvhNBf7vosArKUMUDTATUPwlMYQiuLJoNiZFAF4b0EDU6cbFZhxTDAKLJQp8Kg5yHtctRE+fXGBs+EceVauUbm2vRczCWmBJO6/gHDMOuMYDhjX976TfcxM1REcoBi417qrekkStD3NTzNPctw0YLPl6l7Tq9kwiXiwQphWCIYlVMcRNSGSOvLn0iXFqgtfm17hQgruj8Ed/3YEVO5N6m0doeklQ5eoHAo52Dp/MZPIuLgNntnTtuzHSaQxDqUHKflzyCTyoEPOI2QBQ84V8sB4IPbbsf1Z5nbUkuKLiqFW1HhduvMjmdlUCnWv6lAp6rsbLpw/6iCqrRpQiMuGH/wuuyY0fObGIINdUnRC9/2WJksaI/Y/qs2IBoCWBLq923HHg8rQLE5MF4GGHXSloCvz3SvB7zzeeNV4VxLiLoNgQshRkvV3LSESQhiAXwLwudb6dxNCPgTwMwBOAPwygJ/QWreEkAjA/wAj1noN4Me11p8cem2lCFa1kVdjTNrWxu5z4docrhJwBF/AoNbyHaonfVJz2waoFhnYKvHDe8DcSFWZbCSaejpEOFmCzxYgqwpybsi2ffI3s+r8+1o4UpvWKaMhOE0N/3APMKbuoWL7/K1DIRXdqMbmdYorq9noEuAhTU4HKomZQiwJYmaqQ072oFEbiqs6wXQ+hJTGMNbNTRM4XzmORgRI7K7fqcQ0W61QJ5Hmj8XOZZOg3ai0/WfVBEIZ2H7bhmBlaiqBQEB1HM0qQVXFno7iKs9XEYv376moEaW2HQUpKR6eXgPARjL0GzKqsGqjnZsCR9KWFiE6Dlt89dFTAMDHLy+8i0K/Ag+oxCQyJPjLOvYCD4A1cLYV811cLvoximp8CADIcBY3GIctYiYgNUEedKgExySqkYWNv77d8XWKYNZRzFoAoHg/DW6J50trObYr3HfuugLOcqxuzYYmsvdX14T+2vGvq2GrfXMdNG24puAIjqqKkSS1wQGUyS3BcgCeZwgYfALZ4ZwhigRNmXrC/n1DC5Pcd4023mS8XS+N71wQQiYA/lEAfZ7Kf0wI+SMA/pbW+m/vet53s0L8dwB8DcDQ/v9/DuC/1Fr/DCHkvwXwrwL4b+y/U631R4SQ32sf9+OHXliD9NqGnaFG8D0J0e66VnW8caMAVhC7HBjFC8EPtj6NJqhJHHWZom2DW6CAZpXg+msfYlx/bn313ke1MN+XM1Nde8QdnmtIZXhgTk4rDXdzmfpSXi7epEj0rnC2OTGjpircIVfl4rLW+OWbAYD3MIlqfFYawMO2Hc/Uugu4Dcv1KttIcrKn0rJxLGzN59uVFB2JGzAbiKqKkcxzSEkxWwyxqFJ/bo2CkERcdHdqMe6KTjFUVYxmusknm+TLWypC/aR4LPKgw1cfPcWTH/6WlfiS+OTlxa1NA2Cq41FUgxHtvSxdVIJZP8b7h0uKo6hGFtYbaNmiiZFFNbjlV0pL/wBcNfvqlYlSFK1tOTunlkYEWLWRrRRNh2RZDLBcbQLpzIx0LV7edAFWdezl/xiTGIyWxrNzkaEqE4PsFZszvn5S7IduKZovTjH//CGur06srdP97j/dmg2US+hvK1gUYPwDrwmq2ZlmvivxfwD4OzC0CxcxgN8K4CX2HOl3JSESQp4A+KcA/KcA/l1CCAHwOwH8S/YhPw3gj8IkxH/G/gwA/wuAP0UIIVrvl6HVmoDbmVwYN7fsl/rhdl3byRAAnhcjBFTig6DbUHo59FrFHq/D/mNuvvmlgzJx/QXQSHm59t5ajLjTBLWkWHYBYmacC/YJTfd30IfCecftc5UHcIsovS9clRhQ5qtDtmfNe16ZpHgSJgd1Jqd1iqoLPULRhbQE85iJnZuIfsW7nRSlJhtJcVml4EsJoRjmdYJacpzBKLE4VSFneBtQbS257hZCEzSCY1XHfiPkeHKDYYHEbqTKKtmokJwhdKdMx8N1IVyLnlGFrzz8Ao9/4FMMvmLcCtw1/8nLi43v3/n3AfDzamcTZSo1hphp5K+o2HSWlF61JrJo3boN/Xu6GbxSFDEThmeqTfv8HtOFndFPhv3v1L1vtcUP5H6jZJNi7zmNCHA+miFJK4RpZaTTbPu0a8Ld4h1MgYTrD6ElQftyjPnnD/Hixbl//1QaUf1thOmucO1W1a037m8rRCNw8xvP39rrf4eDa61/X/8XhJB/UGv9bx180ls9pP3xJwD8B1iXs6cAZlprl7mewvBHYP/9DAC01oIQMrePv+q/ICHkDwD4AwDwMEoMD2nHzpqIFqRtoSTxu66yMm7brEdpMLD6Abg1fY3CFomtEHdVGl0TolhmWJQDr1GapBUYl3ZWZ1637+vmtA4d6dgBayDXdIpRXNlW1BDfWG62ejorHu1Mcl2FWfd2kf2FVWB3UqRMYfLoJcK4Ad9SaXHams4Zoe/83q8+a8k3Grfcfg7n/i609oLg/c8QM4JaajxdKVzVFFlgZlX7opZrR/rAkvY9t07yDe7efcIthi7ZbiMg3+OdnWFZwvlWVdMp+M/mZlL76AxCMt9O73cdjED0el7pvjvOpHeAz4aFT3Y0EMhPZjivYowm84NuCM4geJ8xdC0ploJi2TmUMPNuDOa87H3pjeisQLyQDLynJJPG9cZ16SIPOhRdgBml4FunStqRh/vX/W57g+eqw3416iTp+l6R/fsbAMZhix/KjXyaOQfcz15ryY3nZl5sHNOqMvdulFYIJ8sNFxtICt0CCBUI0+heDLF4apLh0o4dBlFttHTDtfKRFBxJWiEeLY1ZQCeAgAOtNmpQdQglqdm430Nr+FXiHSLm/2d3/N1GfMcTIiHkd8NwRP6m1ZgDdsud6Dv8bf0LrX8KwE8BwA9nEy0VRdsF3qJpY5kUErriWDw1bYxllVo1fgYgQNFGeF4luGw4xoHEVZVitBiicRqNe9oWzv6FUoUkqb1CDOXC+5m5XaaUzJOOKRcIBPNi5P71diTFbxWBSYaK+NMQ9Ko6dwz99qFfQGCTYh9o0ONSDSZzo9SzyLz5bcQ7T1EIA5MMo7TaoGE0IkC9MpVxH+XYJ2HPOgFOAji6haMrBBQA1vSF+0Sn6MZ30SmKookxsLtot0j2Q0i211B3VwhF8WkxsImXgVOJT+YneFrGnmfZKSNvcNlQAAFiK0LbaWLFFO6hDduTh9uuFMfDhTn3vY4Hj1pMzm4QDVa3UI8ukqDd8MA8FjeNSfY5N59h2REsO2NYfCyKNjLtx0pgnJgEHQUdkqRGGLZeQ9Z1ZGJmnOhjxhFQk4wLwVFuVXjhjvlqP9w17uT8HKCoH46f6jY8AHwyvE84azklGYYffLHZLpUUuqIQZYzyi3NMbZuUU+PJmOUFimWG2WK4Qet68J6pzLrFAPy6AAk7qHkCWb+9FumueFdANQBSQshP7voDIeT3aK2/ZwyCfweAf5oQ8rtgerpDmIpxTAjhtkp8AsA5VT4F8D6Ap4QQDmAE4Lb0/Fa4SkFKanZdVoSYtDX0fIXq6SNMr06wXKUbi2bRRriqY1zV5oZ1dkTXZY7GGtHu2vX3kapxaFzWedSAx63hJVrOUlQb5RrHOXICwUrwjVbk9k2/nRT7FQlg2oJ9eoiwi+n2XM39bpub51CywbBENF2hWuSoygRR0HluZBy2iFJjxwSYBSG1Mma1CFDbXbBLhm7OJrRGB2X5iAES+9Zx79DEkRlSHxjidvFPS/N+mU0Qwu7sF1W6U8quU/dPhp0iKMX6vQKq8HExuKXOIzUgJfBFRZEH8G1uZtvFd02Kxt5pDdwoq8TbRQ2GBSgXvlVKrOBCELX+OtuOgJrq0rmquBbmNmhlO5adsxiDBbscD3fvOMSq1ASnaYE0rv0cTkrmOzKAmWeOwxbjIMSs5eisJ+a8Tjbuy1Q2XuDdn6ttyT3LD67l/i5B1YWoJcfYeg+GQYe2CzCvU29LdZe4mo0hLZ1p+AOfb6BNxSJF9XKC6+fnvio+Gxv7qGRYIB5U+PST97GqY6Sx2SjEEzPqUh2HmGYgTO21lntbYaWI35X40R2/IzDjuK8C+N5IiFrrPwTgDwGArRD/oNb6XyaE/M8w8jo/A+AnAfxF+5Sfs///1+3f//Kh+aEJ4neDbbvZ7yeyg7rhWL48xXSZb7TY5k2MyzrGtA02zG+FJpi1kW/DZEHnHbS3o+9svj3rccG4ACxazC1iPGoRD1YYtJsIO9cGTWiDiHe2ks1QK4pJ2CFmEqM91k+7QCaUKq8K45zsnVA0i1toSY2yzYFZIQ0EYEUP3GN2cQn7FWKnpa/1jarN+nGGnG9+zoP9s8Z+PFsl+GzF0CngcUJwFndeJqxoI3SKbRjRujZev919THFIaGJUgjQBJPBxYeTkDFF9fX0EdI1hNo4fphWcBxrjQFpUMkW2o4XPuDRVHxcbGpjxoEI8qJC588wleNQgmiw3KkEaCC80vSt2meYOksqgdbvAn4OYKfvfWpWmFGu1ofMYOA2Nc3w/nByaE/KetYGfq9aSIeICZ2xmeLDOCqznTGHOn8RvGs2R8AyVYPgwL7wNlTmnBtEtFUWervzncP/KrerQf1ZL6XGtUqGMZ2O/YnaGvjHv0AgOKe++JF7O1ybkLimKRYryi3PMLk98he86Nw7BTG0X6ePrBzhLC3zp4rlfIyhMlejuyUP4hzcdPAoweV2lml97M8fyuqG1/rcP/O2P7fvb9xIP8T8E8DOEkP8ERgDoT9vf/2kAf5YQ8k2YyvD33udF69a41kff+joi/ksgz15g8fETvHhxjqLdbMu4eZTcWrccNH0tgaVwlpTgTKKsDPnfmca2XeBlwPrhRMCV4GgtWkxJ46zhLvogas1rSbYxPE/jGklSo20NwlJqYsE0a15X0cZIisy7VriW1PaCOMwKDE/miEe3k3k/lLRoSAthv6oTT3iODsDGnRg0t9SUThEIrRAQhvMgxCML8ttukQbUJUrnSGEqhZgxI4y99T7SAjFmrUZAKRgNcB5pdMogWxvB0YBvLKq1ZP47vI8zu0l8BAx6J+gnZkDO1cYmKqYaF0mLs7hG0QV4XsVGHaULkbURVlWCPDMtxeR8ChoIY+/TE6/mUbPxPbGk8clQdXyjglAdh6xDkMr6RR5ptTEmffXfb29PQoWYMnSKo7Rr8XkMvJ92Phm6cwwYYA7fMjN256hTu5G/+0Ta3x8UG4lye1YvLK2m3z3Zdr9YmyFTwCa8iBu6TJ+e5K5lStXO1rrUBPM6xXCZeWeaYplhuUo31o3Prs/9zzxqUS0yTK9OdqK7t0Nqgtpush2FRnVrSs53yorOhag73Hzz3QDVEEK+DOBPwjhcEJiC6t/UWn966Hnf1YSotf55AD9vf/4WgN+24zE1gB971fdQiqKc54i/8QgpnqF9McTV0ws8nRqS/rZU2rEwaihig+bgQDLb9klKUtCtlVxJhlWVYLlKkdclnFa+q7qCqEUiTMXnWipZbtossVirb8yqwYZYcaeYQUjuqXo4kxgPF3dMhkYdZFXH3nMQAK7qBOWLx/jS6AaT4eLgawhFUXTcJ74JD3CRELyfOmQjw1JQ1HJdMW7D/R3fk9ud/q6KTmiNaWMoHjlnO4nfy+715zB9oXEzwzU/MwKcRxJZIGzL1lSU76cV3s/nGA8KLKsUs/Yhaml0ZPOgRVrHyLPSgDNOFwahyBToVKCe50jPpogeTkESAd1S6Go3stH9TkkKWUVgcWvNmHerujiUsPfgrNINAfCYSdSSQeoUn62ME8n7aYcnlobg7KE6xRCFzTrZ2MT6qhFxgVFsqr9VG3mazb7oA22A3RSlgBpUreOPRmHrkcLVKsGyHBjuoQhuJV+nHHQ5H/sqdLrM8es35+BE42KwBtu4pBiGLVZ7RLoPcYClpOvv8Yj13NsO9e6gav4MjGzbv2D//1+0v/vHDz3pe6lCfCshLK2ivDbtjfJ6jM8vH+DjpaE/PhmU3s/tWBikpUAWbrYonQg0pQochy/+rjE2P1erDJN8iVFvl0+5MNqnXCAMWwjJPDgnTCuIJkQYGjWTRFgZtR7Hzuw2A79rd39zyTDsoV0PhbI6ra7KzMIGjGjM2shYUV09xEciwMXJ9c7ne21TScAIEFOKSQScx6pXZbgqgm4kw367tLOv069g+i2+PGCYtQbBelkTxIxvJMROMU8pMMCNtXXW60YtTWI8j60IAZMQNiFkVGES1RgmKwwzs3BmQYfPVikCSpEFCbKwgZQUPGpBEmHI3LIGq0OkgUA4WYKeCBBOoIWCDmsQmxiBzYVTCQ7KjZhAZw1k+6osbk7lEqGrPJKkxrBeeSWffpjzSxFb/013jwSC7yT8x9xUkJngqKUbOdxtXjuKKwyi2otghFWCYbLCwoLdhDStzkFUW4eWzWBMIk9XEIptoJAZ0eDWJzRNqi3nDHhT7n1RSw4uAlzNxhCK4ZuzE/zq1JzfH9EET7L1xvKz63NvJ7Ydx3RSpTzuwerikM3bm4h3Jx/iVGv953r//+cIIX/w2JPe+YQoexJO9TxHXaYomhhLwSAVkAUhsrAxLRR74fZbX9thHremRSjnF2fD3RBtHXlptL4IcFtHKJoYV3WMy/kYo8UcSljdzNcU7nUkbKf96VpL+aAEY8pIYG35yfHoNhjDaXi6+erZcI4obDFdDHFd5uBMYpLtrjId1UBqAkaNKwJAcRKqDRj/rthOhlAaNSiwtVi4tthFLNApjueVRi3NbK+2c8KASk+bcMl3HDYYJyWKNt5JWN8Vrn3eadMyNe9PNqrEfvTBRNtRS6CWxHL+GNrWaOnqikMzBbFITZWXNHdyOaheTkwlKBgCAKpTa0oGFxhOjKGyq4q2W3CMC4yHCwjJvJfhrnA+k+azm7bkNqCFU0P9GUuGSjDMurtdy4aKs1aLckGpwnhQ+K6LozLtiyhscXFy7VHA7rXvdAw7wGeu5ep4jVUX4qqOcGUP4aqOcB5XG633VRtBKookavxsUiqKoo0RlQPPZa7LBMsqNbqte+b/9TwHLVJQLkCZQnEzRtOGB8/B6wZ7EzPE//fNHMsbiEtCyO8D4JLiTwC4PPakdz4hurnDISKsg+I7btma1gBs75lc39+1JvvzjDhsfXvFWUbxqPHzgX7UkuFFmSN/cY6HDy/vVLndJ1zCHg8XGAwLBFFrOJBl4o2CAaPPmAe3F0sX751d4vzJM1CmkE0LDKejW3OcY3ESKivifb/9Z6cJOul22GvD2TUPUeNxIgEwv1DtioBq5BYENRyUiOoOi+o4qlBYQE2nCNgOOa5DcZcZZd2GWC0yxC+MQHzfbPZQqI6j+OzhhuTfdlCmEESt4S1aQAdgFJX6ghCMKZxNpuBMYlGlt6pF5+DhqkLXMgXbvP6ZFaYfh43dEN2dQF51oRdld+Hm8A4AdCwYk0gGRknIGTe/Di+1lsy2kndvTDrLV2VHOg6NCPDJYoSijSAkw7CKsSgHeLoc7UXDtqsEV8/PbXvb8pdtK/bQ/P51QzQdrt+RGSKMkPefhFE2IwD+Kv5eFfd24biFuwbbAdEAXVcznVo7jTOy2zZXaoIA8LtGNz8B1snQ/ddaGbBwkRsB7WwTaOOU/Z/NzG52MlpYYrYE0BrRZ3bYhHjDHZx3voXlEHRpXCMZVIgGK/DYAAPaJsSiyAw3ikmsqgSUSeQXV7de/+LkGqcXl0gfXYMNavC0BmUSi+noTovUdvRRpwmXqBVFyrVpqzKNgGgsxe32Uq0ImKIIHFDBeh9yqpAFACMhUs7s69w2gXVI3OGgRJIY+oFDnB6qFM13RLzt0Z0+ozVx7pvVOgF0D0Sx76+UseSqp0OEXc837wiycPXsFNd2wRwMi72Po17Afe3SQJkRo9hOiq7yYFQBTYxx2GJpN3KcaL94uy7KPoRuErTIJMdYMsyO6G5Km2gZ0eZ+6gKEQYdhVngFHyOJyDeEveXGsZtE6JC6UVrhUZHh6vl7WHYBzu7pHtJZmtWsDcCp2qgyx2GHSWTe2yn5SE02Nj/9McVyleJZmeMbyxhL2+Vw81G/sdgKlwyfT0/86+XJaqPt+zbjXaFdaK2fYj0/vHO8owlRewkyJxnVWsBK2xqQiANBxEz6gbqjU8zaEB8XifH227pCOsUAKlF1oX9eEu2eQa7qGGFpuHu7ImYSWVRjkFSIByskw8JwEjuOaLBCbMWmgbUIObCWWNuObfK1kAxtHSFKK6hOGaBMlWBWZv7x+aDEYjoC4xIp4NV7HH9q2ISQdQgtKdplulNPcRtGvy+cO7uLSdhhEprzkAcdGNF4Wg5w2WxeluNAeR6f27n3YxK1CKhB3T5K96u13DWcLVKtqG1zAitBEFMNRk3yHvB1J6FTm4o0Z3GF08HSfxecSYzDBu+npmry7vW2Nd2sEm8xxHvXEmcFSKg2QDX1ywkunz7CJy8vMIpXkJL51uih8NxRYdCLx2yEzuIKk6jGsgs3CO53Ff0+5gbi1I2WXYA86PzGdZ/8oAvG1EZS6NNWAJP0Hz68NEhrK792n3AdiKWgiO2xRcqIlHOq8FtOVhvobrce9JOio1k0IsCzVYJnKwDgSHjiObTuPDYWnS5stT+9PMHz6Qmelbk/77UIcDpYIt2BXn/TcYzQ9v0ShJA/gx2iLlrrg1XiO5kQCcww3fXyhWReIWNZDnw1yG2V0Y8sbDyA4GPrl1ZLikJwY0W0lXBm1QCNCHDG5vcS7U24RB60OB/NMDm/QTxaGimoQQ3dGgh9WK3QLlOIJvJzxn2RJyucnRq9Aoega7sA19MxpKQIoxbFMsPVwtivRrwzFVNaoW1CTC9PUJcJpOB4cXWKuUP5fQHvp7iYjjaSoYOul01sWkJ7FkvH43MqNe6cj8MWedAiCxsMk5Xxg2QnwGKEy4YjIBoDrpBw59W4fzHOgg7n8d0WDGlh9q467O/y75LYAVMxSpssS0G9isskMnPKMOg2rockMBQMl9Dd7Kxf7TiB+OJmDPZyU/LPVXXFIvOi3dK6M7hkuiu8UlK67ikLy208NrM+TQuMrKar3/xx4We4+8JVVbuANdJ2V2ZthGKHfrCLXZ+HMYV4sPI0iP7n60cQtXh88QLF8rCu8LFYawUL3zJ219i+66TvLsOoOjomWHYGZJffGNDf1Wy8E+QErA2T31Z8t4n5hJAfg9Gt/iqA36a1/qXXeLn/vfdzBOCfBXC0H/xuJkSiMcmXCMPWm/SuqgSLIsPCqalsVxnxyit6AOuWkEuK+zhxUhN8thyBUYXz0ezOSTHjAg+yBSZnN0jPpgZVOKpBEgBtBypr8IqDZxVEkaCeDjcWMCf4DBinhNFkjvzBNWggTAvupsOyMElxtjCIWieHFfHOy0ilwwJBE2IxHaFaJbiajXG9ynwrS8xOoBTduXMXkqFqDEjoEIChlgQ1CAAFRgg4Bc7jGmdJiWGy2phzDp7bBWc2RsKNd59bhLfd1YG1x15/1nMXMEUjAkybGJwq5MHaQxCwkHt9uzvQj4BqP2++aSkGnOM8bnCWlH4j1g8jgSfXwK09C2q5yHA1nQCAlwF0Qg1CMq+oEliiubBtcJMkbicqJxvYJ3krSYED88eHwxnGw4W3OppPR5guc1yvMkAx4A7uG47P26lNo966DVE08cFk2A/TNuUIo9b4BVr1J2CTcrIdQdQiQwEp+CtbJnWW9rPsQpwlK3x09sJvOmezkUeL+2OlygtzMLbWTd3WaPWv3xMVn86HUBaAs+v6PcRnfFPBowCnH1283ot87bWe/f8A+OcB/HevdxCA1vovbP3qfyKE/NVjz3snE+Ku6A//N/3h1C3ndPc4s9Bqz0ETiqISJlEwJlCKANMmwqwNkPAcEe9wdoeht1O4GQ7KO5Fv1zf+YT+4fY9rbPvIoS93hZQMz6cnuKoGPvk4+a0v5hNkYeNdDDiT3i7L3cAuiWxHrdY6pQE1s0Dnc7gvkqBFHghvEHzXuCuqsG5DVF3oX7uWHAOnaOOEuf2szDwn5aZduitSbgBDWdD568h5cR46Vgf4cKCX6eUJrmZjPC9G/pz2o9+6DGDARs7LMbRJsR+OjrEv1kAb7oXl9/FY7xudIrcqRGXBWJwZxRsnGu82o/vCiclTtnb6APqzVr6RFHnUIHmJjdcAACAASURBVEwVEphq2IGPjt1ruzYpfb/IQ+HMvYH72asJRVGKALMy823jTjHkQbuhuuPiPibJ9w3RdLj65ou39vrHQmv9NQAge7xTXycIIV8F8PDY497RhEi80osTqQYMQitPViiauAeg0f5ipluLb0DVLRscJw/mBISFbbftQxW2bYhmlSC0/ndSmmrrdLA8aBTqbF+O6RkKxfBsdoJVHWOyyMCYxMIq1kSWy/gmw7WDKFW+QjFoPpNEC6ey4c/HATfyHQuQazU5vuK2QPN2rKtGs+j1OYtFF1hxAAJOc+MbWTaY1ylqyT0J3bkbmGOiVlkmtGLdh+XkjDybwllcWzsjueHP55SD9u38gc2FWli91UNtyV3XWrHMjPHwju+7nkfA3Fx/LjlsJ08mqfeOFFY8gjHjUu8EGlx0ioKRtRLLMdCKsLQnKcxj3YYwsFzZwR7qQT+cCP527EqKYb5C9HAKAGivhwirCKION6TxdkUtDW/1suEbtCuhCeZNbPi9M/NZXXUY2Pmh+yz3NY9+Xhm5O6c8BQCPBkswqtCtMkhrGXaMy/im4g0Q888IIf1W509Z44XvaBBCFjCLj7b/vYRxWDoY72hChHfMdkTeJKkRxg0Yk1Y5JLJybNJfzP3g9kLfpXxS2XniNlJsnwp/W0doC9Oqlb1dMrOaofvCGYPuIu26G6RoYjOPaSPM6xTnuQFYOFUOadGMb8I2xs9G2JrjuLbLWSdDwMxIhaJgO1CjADYW/E3UoPLtxUMizY7v56rSogtQS+pJ8gCw7LhVwyF4VsXgZLShxem++z6Zu5ZsIxn25eS2I+UuGTYeWdoPlxjLxvAet5Mcp2tYvbsOpKIbi+P6WI9XylUVQ0rqLcX615YUDHWZblRb/WBibVAtpFEpcj9vOsxbZDZVR+etBnRkNWSb0FeibkNIqUJsN20OddsPd4zbnwVY6+/6vwcCYhUjOp0jeLgAPbFuGtk15E2CbpmCVy3aIt29aZDcJ8NlR5Dyze+8UxRXq8xXgfO6J7HHXi0Zrt+b4GkZ44OsAqfKg+NqYQzA3XneVud506H1G0mIV1rr37rvj4SQ/xPArr7sH9Za/8Udv3+l0FoPjz/qdryzCRGApxZEYQvGhRej5tbjz8U+R/J97UWhCXDHVgoAtG2A1SK7nzahlXPaxWF04fhWzgKJVwmKNsJ7w6nncb3KDRRQ5b0Gt2PbINhphM7aALPOKJsMuIWeU4VHSYvziHoI/3bctYJ1x5QHHaotYMG0CXHdMqwEwUlIMOAmSZbW38/QJgieVzHONEEedF6CT0gGRjQ6mGT4rIpx2dBbdkcBWbfOA7tYZoHAOGyPXktuEXWPc/6VebryPEF/bu/RIt4OIRnqwlh3ZcPCoy+bVeINas/GM2SHqBrWxxIwKGkhjQhFGjZgVCESxt6KEY1RvNqZBAa8w1fHM1zVCc6SlSGob33/Lhl6rl0bYldz99g94ytEq+5Dktao/lhEL6oOJGlB6xBaMKPos3XNuWvYbehOQgVGDQran1u7Cbi2M8O1eLxAzDvkVpWof2+4TkceBMj59obbgMWkNq3lWWuszP6+00s/Ox7FK8+RjrZAWm8jeBzg7HVniH/38J+11gel077b8U4mxD50WEiGfY0Sl9D2tSMO7YD78zKpCWZtiKzMPQii6QI/ZHfvvw/V54WZkxaESaiKQbd8Z7vUVVOutdY3qnU39qFqsFMMTKmjiShmAjEzKLhOhT55+Pe3SE1XMZjXNtSE7XgyKCE1wdcXm2oox45hPcdbLyZnSWlVQ9Y7dKkJVsIkPs9ZJNorDnl6RE8bdRfh2rRfzesIZZRoWO8QTWvMtGQd2OdQ5IMS2bBAW0cQzxlQJ2CE+ZnZeDzfaF0yZtqH53F1UH9VagIpOVinNxKo+044k0h611pbR75L0LQhMqznzIeQy/3vJwwMcX7QawXviv4988FoiloY6zQ3jnBV4PZ44i6hBIcS8IAgygWSB1P/d/6hhK4U1JyBPltASwI1j9FOc6yuJntfN6AGmDUOO8SM3eo+iB6X0X2+wCrzRLxDnq5wenYDyiTqcq3B6vin55ERpjDPX2+k+12AnFN8NJwj4p0/N669LBV9pfN13xC1wOV3cYb4vRDvZkKEkUy6y27bkfdvGcnahFPL3YLRG69hneuvqnTDcug+oTpuuGZSeNrFPn1DJwl1V4rAduySqnLBbLLIorWLeGcpCts0gWMxDls8yec4H80gJEPM1hqyd42+y8h7+cxoVkoGvhzhqkqPuAlY8161W2btruGEAxIuwYlxWL/I5mBU7RWiHg8XOHn0EtFkiW4xgJQU8oXZfY8HBSajxe05HhemnZgWgEX67pslfloMcB5v8l/dNZFEzUF4vlNtchu0Q/QLL/CQrOd8UprrwLm79N9fSIYsqvHe+UsMT+ZY3IzwG8/ew6zMDgJn7hNLS1FwohXJgynCDxYQX/1R0GoJ/I1vQlUMzRenqKdDLG/GYFwgHuyn5cRMYBI13liYWTGCoguMHKEdDTAr/LCdDB3CO1qmqBY52jqyNCLpOyaABelYv0jA3CMAkActxkl5e3Rj29huXPG247vJQySE/HMA/msA5wD+EiHkb2mt/4nv5DG8kwlRaYJGBBuOFHd63tbi0yljEMsp2alPyakC1wQxXc+xrqoBBrzD2NpDuQvZLX6MS4S1qRnzk5kXZaaBMFVit7sy3A7XsomZ9Dw9AEeTdyM4BGEYKrpz0YyZQBK0vs0XM4FOhZg2MSIukMuVP1eHEnLMJB4NloZneXZjXD7qGMsu3HC5PxZGKJzjYlAgiRrkWYm2DSzxmqOWu90FXEgN1FLDgXtex5EBMDzDs7RAnq68Gg2jCvM6MRqyTOLB+RWG5zdI37sECQVoIDBYZBguTDIfJJWf5W2Hm0dlYQO0AHA7KX5aDPBJyVHa+eyaLmIFJpoIwyPXEGVyQ/qNcYEsL7zWrbsXXDLcdbxhHRnh/CqBtPqsnEmcj2Y4efQSyYMpeNRgUWR4NjtB1YVesMBVXK59K60k4LFY3owxnQ/9uQJMQiSnObqzD0BXU0Sn30D78QDzzx9iPh3hcj4GpxIPT68xPJlvzCPd+QbsZjBoPWjJUFsMB7kvVxdx4YU/0sSo5ATDEszSW4D1JmMSrzBrQ1NlUuWT4dphRJhKkncbnwmA7zbR15hP3ie+2zxErfXPAvjZ7+IhvLsJ0VV9GztYwY3Hnwj8HEpq4qXYHMIOMOoQTtat3zLZDpeA3EK77ALD31PMmPqGLZJBhcHpDDQQHiSzizsFmJmIwu2kqCw4p2lDD3KIuLBtvPVlPIlXnrDNmPSP70O6QaVtf1LjySh3y9u5cOAhB354nZvzrjB2YC0UXmviq2/AzIavyxxTy6vc5VEIOBUZ83NAjdyWW5TWqFLmkarOoaPa6oRKbTU997TYuZ0lpWGD08kM48cvkTw5qiN8r3BJsegCXLcM0wb4/9h7l19JsjRP6GfnHHubv+4j4kZk5Ksrq7tqpkaIHkZISANIsBjQoNkgxI6BxYglEgtmNP8AiNVISEi9YyQWLFiwQtAgsQE1Et0aprrprKqsfMQj7424D/frbo9jZuccY3Eebu5ufq9HRkRmT1R+UijixvWHubnZ+c73fb8H9QimTYDMbzc4lIsqRZzXjkdYVRGWVeKAH8k4RzRbgjCBajlyEmkAMDu9QVYHuDUJShvbCpdEtlusfdBWLdheO7WF0Y3to0yzUY6jz55CtUy3leWmhdq2LF1bB452YAFLVRmbTspmV0bkMfJlhvlqjQYdJSXGuHWSdrTcTIz+HbQdey2CSifowIjc6Zh0gkL1NprTNMdZG+Ci6rVSzTzcd7PlNSUMgAMCCkkhzGflr7GJfJN4X5Rqvmu8lwlxO5pWVxSB36KqQ1xV6U4r1EqxOfRmE96JcuxHn3ANrN0mokAb/qaz280F0gzexTxD9WrmEHPWRJj4AlIG+m9BoSRBW2tt1JJHG4LFjEpMBhLUqkzWxPmeoalP5HqzYFRO7GJkX2+7bXNoArsvauEjFwxckoPPrQ1t43SEWY8nOXRsep66aeTLPA8+6ZxEXCW0ZFglmEmOGlCxaAnOS4ArhYiQjdfkEvA9zUPdroytsPVsvMTs0eHJcCe53KNCYqkta+H54Vg0IWA0chmVuFpOMOcJzrJbxDFHfDpHcLwEjWsowVCXsRZFOF7AHxeQVQhCJeZXRzttOkLXsm9Skg2lonkd4YztB+y41zCV1fFPniH8eQW0AqeCovnLn7h71SZGi5i1QakG4/QNtA8JR68yxH5Rh9+5BdkqirYxGwhJd75HtaUo82h6o7+HKtmwprLH1I81sV8DjQC4pPi6n/l1g4XszUE1v3k7x/JDxe9EQrSxqhJcldkOStGGkNQlDy6p4xgORZ/vBkBbxfR5iUQhCBq9G38wd952SGOAGUL29RI0asBf6YE/8QW8QM8QParQGaSpqENURbw27FVkx/OwH9Ls2IXzSDTHTCUg1wr9QlJURex2+W8ad8lUSaGPZd74uOQEmX/H7G/PsYjOw9M8daT9/c/HBomeEThfPwumWbW+USHRrcebhmDR6GS4+Zn031x6AAimpkrerpRHcYmZaZOSI/0eXbXmk24cX28B3V5M91Xq9trSx9SBEU0NsJs6akQULDrYJkVGpXP1oMRIn41KkCMBhhJRtYSS1BkSk6yFqnrKMj09XRs2KVpT7LyONkBA9yUaRiWOHr1C9PE15E9+AU+2iFa/xvF8jOKr2IHSruYzDTTqGVpTJhx143CE8np0Ye3YSJ5o2tM9OIOYCZfEmKnq7IYob0KUPBqcwVp1HHuN2Kr41nQ1hhDsjOpNtLWJ6p9Hy2st73FoeZMQtcCr98ft4jvFe5sQrW8hoAEyeR05vpkdkm+H7DzIXgWw3Q61i7B2VBimJbjH9lpMnSRA06ED4BUV4OvT3lVw4BnbTiUwABuTDK3YdlnFG3ywOz97z69xO2K/wTTNHS3DChfci/i8w/DVnsuUdYjIWnvUtmntjvm2jnDJCa448Dg2LWu19qts6wBNoxO5TzSAxTbgrMfeovEw8imOA4nZHcr/8h4gjVDEJcN9LVdgneR1legZN3lvYwNxCGnaJr4hcES/4rKvV7XBWvzBJEOrg/okaZAyH49i7kAZABxB3KKDbZvyPg6jFNqgtmvWrUfd1r/7mqibAMsqQWHGC7Exz7aJZztsNW+vtU4On/eijnDLY1dBWUCMvY6G2pQQHJ6oQZoKneigWs15DI1ovG84n/c5iWwHNSCqPsUCAF4WI7SKYFklmC4zpMa6q1kl4EWMwtg12c961/1lxQmskfNQ2GR4n2XZm0Z3h5DG70K8lwmxlsT5obkW4ZA+4D1SX8zrdkAY1t5HK1Tsamtux/Jmspd8X1+fuVlis0rQrBJt09RDl4qtluZ9YRdnB4phcFVixFqcThbIRsMajzaBSbKWodoXtkragJGTTSHu2zpClGtDYd4EmNchiq31KG8i5Ne6FUyIwqLI3E1vQUsYSDh2tnuf5uh9QQkA1WHkW3AKQS4UGNHVoVOoMdql2hIqQFFHbvbMhb/X8aSrAtTXkw0Pyn1hKy5rcHtXfJhURv6s1070OsT+ekG9i7oBaOEHUUbO7cQaWUseoMmT/TQhSbW+aT7CnCdoTTJMWWv4csNfCJcUt3WELOD3qsYA+vuoWx+VAfo0dYBma5YmJUWzShA+vUZw8kt4ZY72WQbBA1cBxn6jQTB7jqsfraI71m+zqERoPputKK/KDC2A3xiwkJQUQaD1g4dEBmrhO85rPywqPfDbO5Wrvo/o8FaI+f9Cx/uZEJWHZ2WM05A6zcRDYzt5RlS6xMk83XbLgtolWl+wHXj89lzAwsQJE861gu1ZQPntmqtHqERdxgeLE0tFIBTVIA+jyEKlJclrlN309AbRbKmthG5HKG5HWOXBnVSMve/XeW6mFdFd3dFKMKfuUQsfq5btrdqui5EjIdvwidJJ76Bjea1D3wnf6wwR3zNUDyCicPwxqXRytJ85r6O7qS9Nh67y0cxHqJaZq2ruatHVTYBvb2f4Ypk61ZLt6DuEuA1M57nqZZvyc1dSVC1Dtczw4uoUANw1KatwZw7mniPpBljFtmlT1iL2Gz0D2wOsEZ32AM2bCG0dDLaT+6H9Kg2KdV8rWVLURYLw1RTxN0/RlQqSH0MJBsqEm4l/FypURAWmcbFGFAeNk7PLVhPkRYbLOoDoRpDfPsGDbLlz71tuKBf+zqac0XUyPNTa6btSrQ6NH0E172FUUuE3SwKehjgNNXhmOzH2YdQ2qNcBRAJml8jMgjwU9ADpKhtSEpcULUydUYnp9BbJOAdhAqIOwYsYVRkjCFo9K2H3W/S49zDJ8L4gzEheDcnB9W7YcMDdYvv9LELzzsd1nnPPKASBUJvq/3ZjkRs7oO2qpx++1+E0XFtCWeQfJVpTtBQeItq5JHYaKSRGteYk2v08PunA+2NgTyfBzLeoVIVHhn/3vFgjWq0gs7WNGkLddtKDWCbg8zF4kbi2dHBHQpznI/zlYor/79YDEOOzcYEno1vc8hirNgDzOoy2aAH6JO9+B4kRY39MJb6Znwy+n8hjXL48xVdLfW0GQYsZVVByV1nGRr7MMF+OsajSnd/dx/u1fpi3dYSqiNFVwb0NulZtIk/7lA0bSlLIKoR4HmqRi+rN2opWjrAv3jA0F100AS45gVQ+AH2NT0KOLODu/rHH6hMJ4el1xeqfZgFHHNZufLEvvg/KBaDdLk5/+oagmt++nWP5oeK9TIhAB6EsB42AS7Lj3r0PLGOT4n0Cy1r+jUB4hydFQFcBShGs6hCrMsFosSt/VVVU70aF3o3a59iwiXiorSkNad+GBdYIGUFc6nlhZojDVRWBG3L1okqN4LXmWFm7J3uehEmAdnGqhe+oCvqYdLvRVjWi85ALhtxUGZWgeFYSnDc1PozCjURiic723/2w817LudtObIwo+B5BRADq6yRmq1WhCCLiuTZu3vrwiXLoUvtckHXb1ScdRr5OrGdxgyeZBkOcVxFaCTdH3I7L1QR4qv+dzOe6FXm5bn0eUn1LpeeZEfWQMolPJjd4eHyNUZ7h+fzYUUWkoRXZ15Wdt3MjB36LR48vtM4nUfjq+sHO+1nOHDMgMGp8E0UZ7ZVMszq2sd84xZzXjUow/Or5R4jTCg8nf46uIbj9i5/g26cfrF1pvA4rSSENCGWCcq8FmZ2B7hOyAIAXy5m7L4KodpZW22EpRoe6rPjGMDozLeNtXWRCFBJz3K2igDlfWbg/GUpJnH+rlbf7PpKiqFu8/M2PoJrfibB6gafh/ReWhUT3bz+hCECUaRFt8oa2H7svhto+q1Lzk2aj1cb/N4ZHaCsLu6BKRZwfniUHD4WVb9NtJ93WlZ0HeXWKcZG6Vo4wFAzb9k1Zh5C1DvyxlqzyNmThNOiDohC6Qou2FTY2hLcZnpUUz8oWvGsBWLCH/gx2oYqo2GkrCUUwN5uZk6jFacR3vBE1qtcDRefEvRlRDvkLrBNrJdhGQrMJX5pahXoASIdHcYMnaY5HU+1/d7qc4OsixE3tYcSIqSA2z/nlagL1NcHx7eZC+7qWPREFHiUVHj+41DSOuTZ1Pl8cre2p7PUwMAefjZc4fXKO0WcvAKpcK7QZAGVZk2xGtHiEx6ROinUAysRg25SY687eJ4fabvWDS4Yvvv7YCVZcvFhXJv2NnkaE66R4TFc7XR21R2BiKC7yCYSiG16iNvquLWJgg7EvNJ1Ha5ZuU5YCv9U6yuY+sqbUIRNIQ75Xb9gmQ/u7IGgRNC1o/W5p8133Y8v0vUyIIaH4dGTQgeZmfVYQPC8FFjHDh4lCyu7+5n1TJVoiOZcUzAhD74s+4TbogQvsDHAbzSkUxctihEowfCh8nM1uNl6vboKNAb0wrcUrHiGiGvW2bzbSny9xyVAJZvh3AeY82ajE+nNTt0PvVW/AGsRiATpcMuStj5UgGonJCLTOhd5dZ37rbJS+rfS5L7vhY91I6lvJSqMrPZyGAqfGYimim8Lb9vMNhf08juRvUMY2fNJBSg+UAK2072sEuEPuZjsRlVi1Hp6XAqcR3dtKvy5GqFsfo6TEKCsGH7MvKFGIiMJR6GEa1IjSEizhCOsAaVwhq7hzWegnISsjZs9jnFQIZytH9QlnK4yz3BkPb4dPlAbD3CGkbV3qKVXOLs2S2b9LlSg7D7c8xpe//QSBSSY2RknpRARqg/ru82RtWHqGFpgY/j6SuELTaqrSnCeOpD+JKpd4rSav7RwAdys+UaqcSlTKKKaBnunaGa4V57aOM1blxyJeo55e6XYs8wyLQifEaZp/L3Jt/fghlWr+KsT7mRDpOtlJBcMx67BUDbKWYiUIItrhioeY14F2Oo/u6eMbwd+hVqtdnCy4IAm0Qk0Q1ZCCgsphDVA9G/L17IwnSIoa47TY217T1kCBW9StR5v9nY3+AqK1WntVkmsJUafZCNhW8VpUeF9YFKo0ouIaJONBqg4g+jxNgwYncYmIhrisA8gOqDqjMuMdBntnVCICcBpxCEXwKCk3zn0W1PjgRBPg6yZwHM2vb/eLOLtzcA9NQna6vXrLY4zMbn3RBJjX+nOs2rXKTQu4NpgNn0hQY5+0oQGqyJ18T0YkRr4AV0QjIw9wR7HGzf3IVxmSyxmC4yVAFarLGZZ5hutihODbM4RpiXC1wuKbx3h2rQE12wu0khSNkXaTgoKFDZQkoGwXLduamSoAhKxFUwcQdQCyTFEXCZrWHzzn1Otcx0EpstESTSJNQ7BVrXXH6IelVklJIeoQkhsiO1+bAgdhg+PZAoQoVIaKwg3aMwv54NhhZObYsvOQNxFCf90xsRGyFpnfQnSeAdrxwbl7ENXwwwZSUHeOt6vT/trQtD4qA4RKDqRZva1gkY8Hb0rM//LtHMsPFe9lQkyZxKdZha/y2DgcAGJPL0B22hYIwN6kyIyk211zBZ8opwOqIdStNjU1dgkWOv5dwrY3qzZwC0slKOZ1qIW4X1OzNRcMGRM76FvL13qTyJjASVziOF1pW5wiBfUObxkyQ2mxEXXeTjI8TnJ8+PgcRx9/C0BTVkQdoq0DpOccX96cDlaMumVK7xQQsMElwXmZOjTnoqHI7+B3bCsfDaENhaJACyR7NjzU6FuO2GEC8VlQYxyXSCIjxG7mwbwJcH1xChY2IL7A9YW2fpKdh6+uH0ApglFa4MXVKS6KbMMjEoDjvl7NZ5iNl/rzJByqZfDDZnDDZDsQjEoEeetk43iRaMrBVlu1b8xtKyobYdBsWFRJQR1NaGhjWRvFpdZwAeti0/OQMoHZZKkrxc67E3mrqz6NmK3aALVgeLmc4hG52bA+s9+VrQ7jsHb+kcpIPdo1gIU1fLH2Qd0XryOc/y5C8BYXP84Q378IaIvPpjeI6ARfLFNjkwTIgYYAlx6elQSLNsEviMLUJBcnrWSTYW9B7s/uGJWgBowS+816/rah/yjdPHA77ADftlmDoAEdIMvXBtUYM+FI2gAwNwailgoSstbJSg2FlSlzAsN+cydaNglqzML6Xs4moE2B7W7Z7vA/5TEWzQyLJgBXHYQKcBphx6nBxvZ8Vnh0pzJ8eHyN6eNXiD65RNcQBNUKkusFseEhasPls5sH+x0KRcCVbu3GTG5I7lHlOVALAGcVdV7oeeBKEAg1XN1e8QirluFRUiHqzZFsRWH5hbc8RhbUezsAdp7Xf419Qb1uL39NGS3b+fkDUCZQVPEGAvmb+QkwP0FuKhCr5OKe3zLkywy/un6Ak2KEv/bx1/CY1FxFtissEFGJ3JzHqypBxFqkywxSUA3cEsMV4pAxdxJXSMc5wrQEMYhXS1WKhBFvqAOgCdxczppwh3XgtHmBTS9FyrRrR9aE9yZEOwuM/QZ5E6IQPhZF5pIcsLlxi8w9HwYNmKROd9SuAVYqbvs72g57Xu130a+YLUXjTTesd4W1lv9djvcyIQK6/aR3cBI3jTaKpbC+ffoxXHpYCYJpAJxFuxVTRIVRvI8cF87qlG7v9l7nQrWE7nkdYdH4OIlqTKIKJ8c3yI4WqIsExTIDlRQwu/61eo4wDhQUVzwCl9T58p3N9C4WiynyJtpZeC0qtDUAE58oPYeE2KuAkUQcH+EGrToxQBaLGtU0Cm2+u/+zfvbw3Bz3VCM0FcXPxgVOokp7MxpQzZvyqyyvcpWnWFSpW4DtufFNyzuiaxqOBfGsTOUdC4Yr7sM6Y7TKc/Y/rdJdBt614DJ06NncPGca6OTq+w248BGyFrVZGKVRGeGSgVcMIWuRFGulIPv3dTHCFY/AiNIbIEEhSs3Zq5vgMEWcnhpMZTof/cXXgpHy1l+Dj7ZfQxJc3BzjooogOg8f5iksRlUKvVmzYt4AXIeCS92Gv+Ux0kJXa7qVzQY3VFZTt38fWdUiLZC95u0CMAm5MklZa34GZs5eVRHocg1GkYaHaJNiXxzDivZLRcAM7adVdMffUtOKGOa1RkUnQX3vXPh1ubyAToTpOHdyeDYiw3sEoGUgJcXkDq/TtxE/EvPf06CmhTkNGhwFFIuGwPcoRNcZoIkHbkSST0OJk4jvzAf7qh+XPHYzRGv8C6z5eneZ8vbDehn+8voUK0Hhex2mQYNZttJVz4M5wlUCeiFRGLRiySP4RKIGwySqMMtW2l9wcYTnRYaIChynK8xObtwCIG6Okd9xc1qTXDuDFL1kJ7ZuzCTi+GRygy/mJ8a6KcB5FeHb6rBW6GcPzxEy4dCuae89pVmQ+gvJ6+6CVcvQLlM8f/oBns+PnQsGMzNSV3V6nVNUmcYFQqOHmTUhqjZAbCqZxR0VduXVeF5GOAoDTH0GbgA528nllifr6qX1kTchKkNDqcTJIAr418sRFg3Bh0mLvNYKMgCwnE+wKpONTYMGBAYrIQAAIABJREFUOe3qoNrzOMR3tM4oryus3g9lKiDrAXmS5IaCpDVT7TEujbtDUUeY1xHmjY+IKDBjlSY7D7Vg8Il2mWEAYLooVtVn2y/Shh5FSFCmkdhlFYM3AcRispFcs5Fuu/aTYauoEQgINDrcnH97fgC4n4WkDpX8Okha68RxF/qV9LoI6ThHvOXsYZ1G+hEGDUZvyVNyX/yIMn2Pg1GJmAmcRC1umhBXtQWaaGf1lHWYBc3OPM1KPfXnOH0koxXhtfYsofHEO2QHLyTFn189xAVnSFmHzBc4SXKcfXCB5KfnIEc+6O21e7y8mSDwW5RNiMeTOabjJaan2l/QVqkRa3E8WyA9XmjhYrOwyLmG6dub2c24BJziTjgAyulHaNpxYdBAKIovFkeY1wEuKutmr5GalOgk22+DWdV+ShV+/8kzJ0pgUX+18DfQsMB3uyBFHuPlNx/gi6uH6++IrP3mbFDT1k4CDV4KgkZLblUxwjoEayIDQIrAJRmcNRYkR6UyPM0Z6IjA9zqz0CsHUlqLDUSOB8rlukrKBXNkeI3U1Rs2G63yUAgf86X2/ZsvxxBbQCmb5DdQyz1azH28tX7ytvO8DZ/APZuShodYVQlk5yGimj4Q+C3CVie2vI4MGEV3HPJGV9OF8ABG4Ku1czz1KKo2cIhSG9K0QIHNpGiPjzABQk0SMxvAZZ5pvU8DZgL09Rcn1Y50ohUIKISPqMfptNEqCoh18tTgMXLn/d0/fkeJuYdu00+G4fEtVMsQG1GEfNWjXjABe2dsA4veZrDQx8Pff0NQzTdv51h+qHgvE2IHz+3OW0WQMYE/PBJ4HEdYCW0P5HsdRmaOdF8kQY0TwO2K7U7cDtC3qRR3xW/nJ/jNKsJ5Cfz+uMPPZ9f463/tc8z+td/Ci++uMgO/RTrOMfnZN+gaDXywrgvbBq4nZ5cQkmLc+hscRuB+RZG73LmFIrjg+rI5CjucRcK1IKdBjYfjhQN53BXCtKlsMuwv8vcd30ZIgtsXD/H1qzOcl4mr1L7rha0FCDy0nQdqzKEB44loZtC8a7FoKZ4VDI8TIAVc9yALOU4nCyyLFLc8cQmiL/JQCYp544NLD4vGwyXvwJXC1Nfte/fRFHEi0fa7s4u3E2cwGw4pNYLV0mLSkGMyu4UfNri6ONUdAOGD2i6AIoj8FtOgwYfHlzg5u0Q00wAaQhXisMaZQcgKSbWANuBmghvnzNwL2wkjN/O6vGVYtWZTRtaVlqUEMeGDEemMcKWkaMy6LwXV3oUHIG5XVQIufDdLJ1W8Yc5tjxWASXQEdgnc7g7Z8zvyW3BJtULQwHXpE+nOdZhUWFweaVStpGgaM9NsmVP+yQK+swHYF5RKRGnpFKuUpAjuELR/02jrFue//hFU895FLXx8MSBX9dl4hbz1nQN2H13XKqKBL2Zx3hn2mznJnCd4sZzhTN0O+AbqRadp/XvJwjEDnqQcv/j5r3D0r38J+Pe3XJO4wvTTFyD/0gkgWhzxpw6BR6jcUOogVOL04aWDn1MmHeKNUIVqmeGbbz4EsFsdWk82YD/ybRp0+DSrcBpVTkdzFJd37mD7ZrJc+Bsbie1gRB5Ucb+NUEag4LaOkAu2bqlL7bDBpYevcoHnZP9iwYh2uT87usZ4pn0H5SsyKHF2SNjWfBQ0UIrsXGv9WbatIGwbLmItRmmByQcv4ZnFlDcBItFuCqd7HT6eXenuxMncKdcAuhKZhRzzOsJX1w8w++1HAOBoGtTrwKVGYFpEZv/Yh1qMrfLADRc0wv0uHH9VYho0CI0uKrD2gQS0SXcScUw+eAmWVSBMoDE0oJJHWN5MkAqKYpmh5NFrJcMgbBCPc+eEIwxo6F3Gjy3T9zD6BqqMKDxKVzib3SCOOZrGx8XNMZ6vJjuzFNl5uCstjePS6W6+WE3xMF1ttBylIrgoMr0g3SPIfRZ3+FtPvsHx3/411McfwytzeGUFNPvJ69PTG4Q/r1D84X8IUl0jLv9nHLfPcP1bndhEHbgvlDCBOGwwSrhGCEYN2EkOcpagS2KMzp8h/tMcT3/zKeb5yJHz7Q1rd6LabmeTRxlRnQw/m964JNhHOlpLqe2wlcQhybCP5LWL6z65vTcJi+a95THmdehARzYWjYfzSuFrXEKiBd1zhURU4nR0i9npDdKzK4TzkW6ZXQPX5br9dajZ8lrcQf9dt77xs9y8ZbWj/Sa4Kw05js8uET6+hhcojCw688LfmM9+MpnjwekVkpP5xmsSXyAwdIJ5HSFvfTy9ONt4j5Mkxy2PNzRdgV7rcGDu1irNC7Y2VnYT+lc5qNfpKtNUsNtt0CzgODm7RPyTV/ASAuILnNQhLl6coWl9LHMt7L4q1hsjckBSDIIWiWmlelRB8gBMaFm9dxn/YmxR3l28lwlRmRvRAijGcYkgaBClJYAEIWsx8htwOUQypggNptTyo/ooPUvYPTeSazYpCqUh+s/KGDETmGWrndcGgD84eQmhCGYhx0c//Qre8QgqXi+Y+sjXySUIG5yEN0jLCpOffQP1+APAz6AAqMdPEH38K2TzkUHlaYSgnrEosF4ypOMSZOShS2J002OQsoI/LpDEFeb5rqbjdlAqcXZ0DS58RDTDJ5M5ZtnKtVcpVXulvoD1rrqPTjwkwfXbjYcuniO/dVwyu2Gxxs8ANo4ZgHHiCCA6rXsaQ6ISFL9ZMVxwhRdyCXjAWM0wQ4YRG06Kdg5HfAEWNYjTCqOqRN5EThZMy6QBMexnCQDoinQaaC3Wu3webYRMGKsl855mrh3WLZKIg4UNvEDBo52jTPQXYU27kXuBK9vx9e3MzZ37koWABpxNg2ZjXtv/PYANmTsutTAE87odKTZgPfvzw8YdHxlw0KBMujbijBq+5AG2Um8jLD9zNl7qDUWgzb+9QAN+GJVoWl/rFhupxNeN/mcmvtDk/tf0c3yd8EMfZ28q7v307RzLDxXvZUIMjbagT+TakuYAL7R+MKJ5S5RK1Ib6YKM1c6ZKMAexl0biTSpgXoe4Wk4wutEQ7arapED89QfneHB6hejBHF3SS4ZNAzQtVO7vKPY//PgF6AMJMTGtYD+DHB2DfXyC5OU1+KsZRL3Jr1ItA4z6P8ljhHIOIq5BygrqokQ9P93xl7svPjy+RMTavQnfhq28nHSapKjqELnhTQIA36og9tXUfbHlqg3w2/MPICXFKQ8g6hBfP32Cz+dHTjLuJC6QBJoobWXBQt5iWSUOIUzN3FVK4ig6EZXO4otLigs+wlUNVF6FuIvxMRuDeR74Adh04gv4YYNRVqDkkZtn2/eYhXo+x7wUvheAKw9TXyHzhZlbMayqxMmTLarUqcEAui0a+C2C3nwtCBrEYY0gaHYWTiko6tZ3idmGnXERX6ATFB6TOyLZFgzEJUUkKcA2Rw1cagpQ31HGbmIWTeCUoQ4NSiWSce6EBdyxtmxHos0CZiiTOGE3yFeZE+2u6tDNWAGN1pZG1F0oAjvp9o1G8b5W79DGjVKJk9kc46Nb0LiGxwb8VgeSoFQEVR26TXYs6aDsnJREq+9UITwm0RkKzj6JurcRLf9xhvheJsSAtjjLblEL/+CefT98IhGHtZPdspXJkDjyUKxahqsqxfR2PPh7QhTGpzeg4xLAOiFCSHS3DcRy4gbxftggPV4g/vglMMnQsXUCU0GMLskQfHoD4gudFPluglMtw6uvnuBByxA1c3i3HGI+Rl0kr71zpVTidLK483m2XdqnAVhHjT7q1ULaZ3u8Ibejj6z84vwDrIoUdevjn12egUuCEwPsmaa5a+Pa1q/9Dm3rkVKloftU63j6RGLkt86+pxY+zssY5yXDUTvB4yDGh6kHn3S4qbUU4H1BTNIdp4VxB2GIqMQsKjE28Hm92GZYtQyzsMHI6F3KzsPLYoSJ8J01Vi4YTg09aC0AIcHCGkowBGGDqGl2gBed0DJstfDdOdeiBOvvsJ8U1R6pwbvC8hv7/qFCEax6NlK+p9HIdwWlEuOjW8Snc5cMbYImvthJCv12MaESE3YLSiXmyzGkIii5oeBQuUbgeh2EOb7tpGhjXocHeakGSQUaHVZl95HV1jA4jjnCZFghq60DZxoueLDByXwX0eHHGeJ7mRAZVZiNVpoU/B30AG0StcTehodOlmlfWG3DaRC4auZyNXEah1aL0S7SxBfwgvXN5jU1UFQb1aEfNohP5wgeLEBiiS4IoIKtNm8QApMMZLWCz4udhCh4gItvnuBifgQpKR62DP64QD0fOZQqoCsOC4y5j3x8SOuynwi1Q0fkAEsAsGp9nFeRs12ySXGvF+LAwvTlzSkueYTcLJgR1R2B/nnuIxTrJlgnRNOOcn97HVLWOo+6OKzxt4iCTx7iIx5j6q91X6e+h0VL0Cpg6iuMfOGOe6PNZZRKqFEZiagGZiRB7ZC4Wa3VU+zxpybRWW7iVZVoakarhRAiKnEaDbsk9KNfWXlsPRPeJshb66TvElnInej7ognQKg8rEaCvcNcOVFxDIZQGXFGqEzyN1tWhRxU6SbR0vK8BREroBKgk1RZX1KpCaef6JOIuGTat765DwFBOJHXavja2rzFhKvqICiRB7QBjdjxAmXytFmZtNjcWUd0qinFVIB2qJKW2tRJ16BKhRZq+y/hxhvieRy18J7QM6PZlOSDfpF3Shy8HO0hvWl8r5pvWiw372idJvgEw6Ku/bAv/Ch5A3UZgyyUIAO/qGvLaQ3M9doNzmwzvjKZGd75E+3KC6tUMSjC3KKuWIb+ZYlmkmPME4yLF2Kh5KME25n2MSMxGK0dmPjQsgVifo00AjhUhqAdANMzrMAtat+jcFdtqKlZdJm9958e4HXrRkhuLpQ3eBIgF05sdsynof282RnGJv/3RV/jq+gGueLRR+ZwZz8W79EvXx3KYn51v5MJC1kJIDcKwlVchCLga9mK8L2jUIDta4GQxwZwnO44fbxKTqHKuJFc81KAZNdR21MmxlYD0gKhXKuZNiLwJwYWPUVpACYZOkv09dBNDyYGYzY7l6Pbd6HXbWgtQ9OX8hNdBGNECG5+Mbx2nlhHpeKsA3LUOYHAzYcUL+mId3MzOV22gq1EDjuJNsGHG3L8vpVjTNWwyPNQw/LvGj0o172nYdl2rqFMN4U2Aqg4x53rXvb3Qtsp4Hm5xCwF94b5YzvDVKsN1Q0E94MQsKqTHe3uYrnDVQxXuCyUY+KsZksk5vNsS8oahfnnskqE/Lu5Ohm0OWt6CLG6Qf/4YTZ5svDZw+M41ZK2bhwAAL15PiFwOqGoc0oqNqBwGVVg+miJ7gTe+I8N3gBr2BQT0AknMbt4mJaWIkzVr7pBES+MKx2eX+OjTp/ji1z/Br64f6ERC1/xVp4V6B0DokPk1M8pKIWsR+i0yxZ1y0JuGBfmcPrxEySPXfn0bERrLI2m0X7m8vyNjZ4r9pCyNe8uqSJGVOQ4lq0hBBzmKFp07ygqkEz3vloYrqS2l9Mx/+y6JqMCDbIlxqrsk9jrevr71xi/QWqstA6A7HJYfXNWhs+qyFJVDNzPrTeUhLqtvL/zIx+M3Jea/eDvH8kPFe5sQAbjExiVzCEc7j7H+hv0dvl3ctpUrmibA5WqCr1YZLjjDqtWouaELnBE9I+pLQgHD9jqCB6ifH8EflWjmI+faEE1W8EflRkt1KMjtFcRftCiupxtoPCWYqxT3oT5Z2CCdrCAl0WjIB9eu/cPCGqIOXysx9jU5DwlGFNBvYW1/NkO70CLKazNaYDP5WNSmrfD76kLblaENIdcCzEJSo0q0lrFjVOLsgwuMPrxA+JleTH8WNkh+w/H5y8cmcYmeIs0ustFjEsRUqYcG9TqEfutEnbmhNOStr2dvvdNkk/u+z7hxLIEAjbUN0TgtcFJUyA9AFt8VVljcthEtUjdv2U6FSHs/ajECwCcEmb9JzXia6zQ4HS+hWnb4bM5UTX2ADaXSJcMgqUB8gawOUFQxQoP6BTRFK6L6OpqEHKejW5wc3yBKK1eVNTzcqzqjtu4v1TI0PMSiSu8UEd8XVs+UN8GOaPu7joa3ePEjqOb9DaHoxi7bLnyxFG53al0fnLO62UE2rY+mDkCFwuXtFBfGtLNVAJdauWRfu64ftu0yznQrsqxiSEkNSR6QVahBD6vE2dYEW22YriHw4t2Fj3z7LfIXZ1jOJ8jGOdLZrQYecLikCBipOSo0haSXsIKkwtkWB+27hK3G7Zy1r+BjFWm4ZINV3LY7uU2C/T8+kRvE76HQHozFoCfdXccNrL33VoZK07Q+eBEjqUKolYbXizoYBFVtV8JKMKiWrcEp37HFFfgtHk1vEOUjA4SJAHhOEacvnnBfdA1zSjO8CRxaVRq9TQtSUdJsEuoQdRNsnHMLQLHKMnbj0UcSA5szQ7r1dcsOWLXAvFGIKNm4f57mKS44A1cjPLx8gKP5q8H5nCWp21CS7mwK3OaQ7rbLt8Mn+v4f+Q2O0xVOjm8GqShyq1K0XNvVzRRBVmpaU1mAv/oI89sxrnjsNsxWBMH+u+2NZ5QiOgka3EBbB5DmnL4uMv5txI8t0/cxehWEBTtkIUdqDEEtBL4ftkoRijhLJWkUQl4aC6BZ2DjlEnnghUOJQhJx5w+3WI7RtL4jVLPeLtjuRsM6gOQBvPjwHaIfNvDHBYgvQHyBZpVAGcV/YmZTADYW6PSzFyA/m8FbLNH8OoTI91eEh1R+FsRk0YvWw7EQ/s75Foo4s15hvi/LVQv8FqHRGXXu43U46OAB6GQ4NUa5xMyGgOEZ01DYSmJaxHj27SPUrY9Xl5recmQe8+rFGZZV4jQ4qbkA+qhZbnz5gio0yiL3c+JkbyM2FLNsBS50i3NhEpTdKBy6YKqWQZQRXr04w/P5MZ7mKXyiha2lsVcC4GZUy/kEV8vJhv4qlxSrWs9tsyZYW0btm5v2kqHvrc8VADBv//xy0RB8fnOC02+e4Emkr+l9oVWadq2eANzJX+yHnQMfJzmm4+W9Ff226ERVRXj+q9/Dye0ILKzx/MuP8cuLDw5qj7ZKU2HyZeaQpvlSa7KGQeMAX99n/IgyfU/Dmo8CevDvFFV6FUSf1wXoGaKtFvPWx4vVFNTr1oLRXoepL8ElM22f+6+ekOmhfh9aXbc+fnv+AX6RVggM3B2A84+L6gCyCkGjBl4ggHsWviBotQnpqIQXCHjm8c0qcefChm2hjv7mM8g//Juofvp30PFLxI//F7A//wb869N7P9NQNK2PZZXsVHKLJsR5GcMnHU56GqcuGRorqnX71C72az3VINDI0ZC3KOoIeb0m2Z9GHJOQYxKVWwLR9/O1wqDB7PQGyckcwWyFdpWgKmNcLaa4LjZbivPVyH02mwSlYK69LjsPShGN3DX8yCFU4FBCt8nQJQwqHUqWMr2Bu60jnFf6/bf9C+8LyQMU8wmeXZ/iq9XYeUJaVxMr76ckRb7MMF+OUbWBW9S5pLisGRaNhwUlGDGKiI5c23Q7qLdOgq8bXAKLxscXLx+BUolHn31zZ1IcCos+PaSdHFGJR+kKJ9MF4rQyoJzXN/G9ujgFbwL85atHBwGWLF6hFlrwPjZgt+3Xf52W+5tGhx9Rpu9tQnzTEJ2HeR3qlp5pq/bjNOoOEgYfis9vTvVO++kT/EFSaTdygy6zVYYoI62NCMCLG3gJQbetkBL4CI9vMaoDhLOVTp7fU9iElYUNrq6PsKwSXFXpRjuoVQTnZYwLzkxFEuOR2RgIQ+gGAOF1GIW1dt8w6ivvKkZJuZGU+hWzbStel9nO/McaNNvY598oBXPJcF+4qtCAvtymYPD13hxVWM9HOD9/iK+XE3BJwKUH3zNJXa7nZKs8xapMnLIOoJPheRXgovJ0q5MQLBhBRLWQunWKB/ZXuZQAEVGAIPC3xL37MWIK0wCYBi0ueYzwlQZ47EuKuqI1HEV6WAK0x5oaGgz1OpyMb53xL7AJ1Omffys2YdvE9rWWRYrrYoRFE+5UhrYC3XZCsY9rlbEHM+4WfRAPZdLNP4FhROvbjCBkePz7D9/sRc7fzrH8UPHeJkQ7v+KSITtAP7K9o3Vl+Up5y/Cs1Bfsh4m+SG/ryIEx+o4Eda962HuMSu/OmTGBbQb0T71AgMQSSDXggC1eQk6uQKobABpSH82WdwIQNhLAgUoXVjR8W8fUhiVQs7CGFAyVMVH9Lri4PtdrG3zUD+uowOUaJbjdirVRVjGaJkDMQ1Am3ILT55MWPTcE1TI0eYKiit8aunMoFk244WV4UWS44iGy3rxMSApRxSirGCWP8BdXD/HP5wl8oq/FqtXi0bwJEFURAlNdLPNMV+pFiqqMcbpKQKjC8y8/xl+8enRvG8/eM33g2ZsGJei5yjDcNASi63Bedbr17OnrujDI05Oea4utYAUP3OxwSK3mdcOOURiA43TlLM6AdZvdVstWpWr7uuyLTtjNkthaQ9YWZHoOTr0Osd/odcFeBwbsx5tgo+qnVLkxiL2336VsGwA0XOD5r34E1by3Ic2C2RrS7764KxnamDe+8wA8DdVaSkywjRbeodEqbVHV1gH8OnBtq52gCl5CALZenEh1A5prz0QvbkD5/Uov952Dfjgi8J5kGAQt0snKOSSMBMMoT3FdZq+9iDKiEDOBzEit3dcKtCLktt0K7Kd4CEmxylP3mrYNGdQBlrlOImUVg95MkUoKXsSo6nBvkn1bsWoDl3QvebiXvL4qE/yzyzP883kI2QHTwFbWDEUdgRGpNyIDm4j5coziNzEYlfj85eODvhdrH/W2k2Hmt66b4hP9uXMhcVF58AnDiClQApzF3I0s+t+tqEMQqt6qbJkVzBilhanG1slGCorFQpsyWwebSVQOtoct4d9V+r1rp58MLYrZyjwCmn8pO0/LP7abPpaUCUSTFYLjJbxYwIsxKA/3tuN3fIT4/ibENQmX7l0wgcOT4bOC4KpWeBSTHfmpQvgbzuz6dffYJo2XeFKMENEE0zTX1ckdlAVNvfDQBTpherIFW5zDa+73HPwu0W/L7JuXUKPAYoXDiSFCb3PxfKJMi3R3hmjDoiYP8UDs80NjJpx4+3G6X1fVqpQkEXfKNQCQyko7EFCpK/PbEaoq2iBTHxrU6zCJKq196xRw1hQAKeiGw4GdUd+VdK6WE/zZ5QP8cqG/D2vE3BpyvvYk9J2oQMiEW+QBff1fribGk9DfaMkOzfes+Ho+0PbrByNAyoCIqh1VoSH6DGCNo3u8UWhvSa46cOnhNOxwFvO1HZs5T7HfaD6qGNb73A5LOdr4P6qcY4R/O9LXKdFyfUO+nVJQXF0fYVFkmPPEtXWFpJjGBWKjqGRR1DYZbrd/tw2YrV0XoIF2WbB+b5/IjevOSvIRX+MHXDJMX48f/LrR4UeU6XubEO8KYS7g6kBysvbGA7hUg9WDHZCjZ3K7L4JQG7JOitRBvAkToHUwODvrGoJOKHhNsztDNOEEgCWBRxVEHjuUqRRspzIs5hOI/yvAtPpTxJcv4JUFxF+0aOZrQM3bUsTwicJHWXFQ1VW3PmqTwOzOfV9Qr8NJkm/wDrejMa+3nWwtT82iWYHNVnJfTLwfraKDikazqMTIOKpsgyCIacla+ggANzO0NmURUagExTND7QmZwBe3U+O20WLqM4x8DynTqxWXa+6jVdgJtyjmpVF/eV6kG3ZogK7cuPTw1WqMkAmUPEJRR3i+miC/Q+qQeR58Aox8LYAe3oOClApYNBRcRsh8gbxl+Lb0MBcNfBBMfYIPE4knabkzj/eJxDgu3TXQV2DaeA+jGjNElSBMwB8X8B8u4dEOwasKQdA4M+IgaECpQtP4qErdPi+rGKsq2dgY+ERpm6syw0Stk6CV15sPoIktTWWf+hUlCpOoRGm/R6PqpBTZi9x91xFEDE/+4A1niC/fzrH8UPE7lxBthXHffO9dhR82iAVHEDSGi9g4UE21jwjfdICQgNi/+HcGgq5adpAQcF3GePl//gKTr18CiMyftx+WxC4kHaRfDB5b66NeTHEyXQwmxZS1eDheYGTURIoqdhxBNaA+AqxBCq5yo2pjdnRf2CrAevlZj7wsqDGOS6RxtUGDsAo5xPxfGGjHdZ+ovRVYJSi+Wo3BiMJXeYir2hj+Uk0et5Vd3voOqAHo5OG0VB2giWoNVEE1oGUgFg3F5/MjnBi91KEQijiKUUTXFlVRL8H3w/c6yK3Ont5Q+risCa5qgbaTmPk+Pky7wWSYBbVz87hrUySFdqXfR2BnUaN1gJ+M0AUBopdzBJeaSGM9P+310DQ+pKR6NmuE2LeDSwZu0McrK+5xB5pWKIJKMFCv044rWxN2q2tr5Q1/6Gh4i2c/zhDf77CCz/2huFREz6EMgvRdxV3Vom3fWc9CwgTitIKUFFFarVF1kqCTCl5RwWMUXVMD8bDKiCij13bV/vL//TmOT24w+UBv7ezg/k3h3n0/Qmrk8FgTOT3HuyJvIlcB9ZNi6LdgjcQkKjFKC4xnt1rc2ezsh4jzTqh9i6RNmQC9x9XBmvTa6DtFgOq5UBLUbuG2rWQALhkSX4DITSNc5nWmjdjtVG8rQVEIhkvTUcsYxcjXSchGq7R6jXVjcAo+vWu8FgyXPMRFRXAaAinbnxQXTYppIDcSU7/FOvWV20CeRQIxWydj2wZmRP9fRBWAteSe1WC9qT1ccP2cB36En088TH2JaaBbkFb4PaLCzNz0eR/iWhKqIGrt4GEFvOOB79EfFyATBTU9AoIQdPyFFnt34v3SaZ+i8bEqUpRN6KTdtsNaXdmNxiXXj/kwERjtAbzYDVRkAEu2pW3Xo4AoNK2/0+k4BDH7LuLHlul7GEoRNK2er5zE2mpnG6xhUYroqUjcF6LrTOvUc8+1HDrbZuvPEjImzGK5e1M3BkzDykiry5i2i3VcJ75AJwm6hgFUoSslIFYgJYdXatWb7rKAmE9cVTgE97etQCE1CbhpAtdh7BXjAAAgAElEQVQmulpMcZFPXCKZfvwtgGEyc9/SqQ9RH4qICmQhH2xnurlu54HLXWHvWvi4rSMNVmoixFWMNK4gpJ7DZQF3PpXbYeHw/RYxo9LND4c+U93zgyx5pHVujc2S5ZACQFZqVwptIvz6aL/txGttkvo8xpUgWDQeVua0ZYy4FiUla8k6n3Tm/FFnW1S1gVvohaSY1xEWLTUygxrBuT3jbTsPq1aLTLSdh6nv7SzsjChkJtH128ir1kdEQyRGON8aMJ9E3N0LPlG45BG+ykPkLSCUwtRn+PlUAejAFcFiS/auv4lsWt9pzvZbosRfbzxqgwCNeYgwqTY2g/X1BDRqwOJvgcBHPc9QLDPUwkfJIwTFumU6X45RGicK2xq3n98mR+s6whXBJSe44nqmmjAKn2zSsLbVr7hkiM3ccQg4JhR1M0abrD0mNYag6dA1HSDKdw6s+R3Ph+9nQhSSOmL1JCrvhPIPxT43gBYKuVBYNAS+R5EOXJwR1b56gLZU2r74LXpzmWeOA2aJxGFSIX18ufF4yQN4LQOkHcJ3wItbdA0Df6VFvQ9VwV9UKahRcil55PwJATin9yGuk20lrX/WclPdwHtGVLcRh5Kh1pWlmNfBXjHu7ccrtVnFhcbwt7/JkJI6tF//fFvLrTjmG+1SQPMF60ZXGLZldVWlODfybRHVjvTpWG8+RlVs+Hlv1mrvVx4RlU6+zCZD3rtcfANgOYToLjsPtzxeIxgFA9/uXX7HYF6HqQGg5AagwyXFeakpE9TrnMjFyG+czis11esV97FgBIww/I2pwM+nCxTCx6+XI3yVpz1ahj4n/XFGySN37Y2OFjubNUqU3gA3Pto62PiOmzJG85uPMOIX8KjC5eef4mo+g1QE18UIdesj9LXYQ58b2m+NoyfWcdC5spWz1+kNs0HMbm9atsO+f4h2v4Zp00HeEryrYY8G1fxwKdHzvP8awL8HoAHwWwD/cdd199j9vN14LxOi6vTFNY2LDU3MQ2I7GTKvQ0Q7RNSDDwLRAZe8A0DQmgu9v/POW7Z2X+/tdm3is3OPfnsvG+cIkwrx6dypzADQFjjmb1lE7ud2mYLfaj9DSyLuCxv3g1LlEI5Crp0/rD+he5x5nnVGIIVuMzZNoK1sepJsQdUiFcM2UZmRULOtRlut2TZ13vrgihysZNLnewEY1PCUkjoFm2OycrY/VvFlWwLLbkqa1teC73WEKx7jkhsfSqKrqSTiCFNt5JvEFbKS4/YAis2+dpe0XQlsiptzRbBqdTJslU6E1NNSdgnrXkv1xfrtVYKiVfo17OfZDt/rkDArcN0hZpttXQDG47PGNNYt/EWVYtGEWDQBROfhvEwxC2tQr8MsKl2L3J5/RiQWTYCbJsHvZS1+NrvBJw8uUDcBWvUEX+UpnpU+TkOCiOr5ajSg7nM9nyKIasTjla4QzXdq72uruKOk3AHYrJ6dQQqKV5cnG84mujUf7Yw2bGsUkmrka19RyUTCOsTM09/TlmKV0y01SXH9mloHlhG5IeJe1RoAlZn2sTWv3g6V+6hfzvD6kuGHRRD5ePKzN3S7uLz/IXfEHwP4R13XCc/z/isA/wjAf/FmB/R68V4mRGFnhIoiROsWVHvzbANq7K5ue/d/FlcaOOG3iEiMiPq4qPTFvmoB6mnFj4jqlpP1gsuaAGfxrgu2kuu5R9lrFVEq97pm2+iMzUx1OUO5zBzRHADimCOIakw+eAka12iXqSMwRylF2vioaq3ALyTFokoHz4MNf1xo09uwwasXZ1Bq7fQNaH7cqEgQlxE8JqGEdhOJ/WaQPmEJ36tWmyeP2GZrqR/9Y7KJbZQVqKphHVNAa4jmdYRC+GA82aFh9NVMLA2irGKXRBdNaLRCKXyv2ztvG4pt6yjKJKLZEuXVTFfdkqAuNOF/n0B5ITwUr9GF1XPHXTRi20NPnxqCeyEIpoHs8WZNixYW4drhSdLoDdyWiwiwvpcs6KNPQAd0wpyE3IlTbAclCtOgweM4xEm0+7hFQ3Be6mM/jTxkvXs18FuUPMItT1zL/BHV+r9KMEhJN9DITR0gQAMgcEmRFzGqMkZRxe46BOAQopVgyPwWWVC7xKhtrOxn1LNaYTYzdhOZsg5pdv9mxapcWRCSPYaY1A7UZakgllbURzxbYX95E6OZj8DnY7yZT8n+aHiLZ5//cKCaruv+196PfwLg3/++j+G9TIiqA+ZmJ28FukPTvixqDdiwc45988OzuMJZdqs5Q02owTksRsICfLmf9vZOwjPgDGB4vkeZQDxeIXx8DRJrbmC7SiAr7bYdJxVGZla2TyB75zXjGjGbY1LG4C83F/K8ifDtq1NISZCOc8yvjnC1nABYa5H2QyqCl8UIXFKM/BYjv9XcOEXAvM6Rk32y9qpjRCFiLdK4QmSqNN4EvRaqUQcSuq1q1WuGwnrXNUb8oKxirMrEJTPfKYp0KIQH3mhE7L7ztNahZPCJJlUncQXKJJKTOcKHc6iWgd+OoCTFN998iKe3R/fSfNrX6Oy3yru3jXca1YgF25EezE1bnBLgw6RC5reb3QI3q9Tty3kdOSDUojdznQYNnoxuEbJ2L0rSOq2cRLVpp96vZWQF3W1nJ2ItcsOrjK40b9cisu9Dob5JcOmBerp9qlWRvA0RhUexbiNf8RBcmvVk6zViJnY4yoxI3co3m2ApGCiVetNUh1jmGeK0QlCVDlT3rmXbbPwVEvf+TwD8D9/3m76XCRGAmVVFDrlmF7+qDRzCFIAb/gNwhqGnEcdZdouT6QJB0KKqIiSF1trMWIaIxPgyf/eqEcA6GXqBAAUGPfaitEL20UvQ30vRBQFYcgPvuoGYZ1Ct5mhZcAglCmUT7q1WrKC4F+vEmy5TjAvNzepXb3kTAdfHiPMMC8Of21cdXuQTrFofI7/FQ1O9paaiq0wCzJsQERXaYcEkxJC1iJPKtS2jKsKySHe84uxs8j4RBmCdDLcXcOZ1GDGJQjCUwsPK8CH3hXW9sHOhkSwQJhXCh3PQBxJBsQK/HeH50w/wq+sHdx7T7msD9J61nCuCuPPQKuJcQjY+j/kupgPzKKEIqOfhUVLhyegWacj1XLl3Xdhq3yrXCEU2TIVPI44PRouNe8S6newci5mrh0bA4a5gJnlEQYMgaBwYDDBiA7dTlwTjpEJkXGTaOtC+hYK9VUFsrojz2ubKKlU1OIk4TpIcjEhc5BNcVDEqoWkYulWsNvRL+xH6ek5oPRf9sEEQUcRJ5RRyqiJGzAMNqjPxrpOiFvd+44x44nne/9P7+Y+6rvsj+4Pnef8bgKG+7D/uuu5/Mo/5x9Bn/b9/04N53fjeE6LneR8C+KfQJ0VBn7B/4nneEfSO4BMAXwP4D7qum3ue5wH4JwD+XQAlgL/fdd2fHfJedh448j0wKvFoegNhABhlE24oUfTDwdgHyMKA3ln731FFyoJB7HA9jSuMj27v1SO9KwiVRtjbLEY+gzeABO3z7lpF4XceZI9v5eaXfT7dHfqJiyrFoko3SOf9pKgUwat8jIsqdoo0fSQdhO8QmxrIoI8lZgIRNQhds7j5oQbHlAbV2AfV1D25MR7o9u22zJaU1O3At5Oh/b5tErnietc/tLj3w/LlAGygVe15W95M8JevHuGShxj5YgdRy4gyrc0QAMUl91DZ031HQuSKOKRzv0r0iUTGBE5i3YKz5/rFauqqZzvfBoAno1uM4tKcILoD+LB2aaxK8LxIsBIEU1/iNKoxCbkGzdjZtanqAGzM7YUcFqsIgwY/f3AOoQhOw8M5sFxodPRspDdWfWeLKC2dBOKh1aGWlRN7KVJSARy7VnFWeYaStWJPIQh8QjcqRSvbZkFGs2yF45O156JTMqK9+wJalzYtcsQ8AMUaT/AuIwh9PPmDN5whXuGq67p/Zd+vu677t+96uud5/xGAvwvg3+q6779e/SEqRAHgP++67s88zxsB+FPP8/4YwN8H8L93Xfdfep73DwH8Q+iB6r8D4Kfmz78K4L81f9//Rp2HyOsQUYFPHlzg9Mk5lGCoyxgND3GSp3h2fbpjA/U60XYeIrOrao36yLzxkTGBWviOH9UfkhOzWAV+i2ycI5qsQONNPVI7M6T07iQZBJrcv3lQAl1DjCffJi8xDBp301kkblFHWN5MEPTmmFYq6+KbJ3h2vWsJZSsx2XnAnkXPyucBwGWt54d+qatJLplLRHZTskFZCWpEQbNx3oKoHtSetPFl7qNV+vU/9V9t2UFRNwu1XQJLARlCjq4EwXWZIb+ZAtA+ltdl5tqeZ2mOLOBuJjXPR2AvzjSfdDlH8e0pnr48w6LZ7EhsRz8ptoqhVYAwwJqEdYhIh5UgWAkgop0jyW+b79pzdjK+dbzLPj3m69sj92+fKDxMV+tkaM+R0i1rW9UkEddIXapl+XxPA29mxm4LgKHxSCeC3Y/7XEvSuMLfOHsBXHyAi+rwpCiU7gRIq7m74YnYOLAZADc/VEYJRppuhN18RVQ4r9Dt4EqDZtqtj8F713U/2s7DSlA3MwT0ZitiLRiReHh8jfHR7eB72TZwySMIRXXrdD5BmJZgUQPBA1TLEfJlhu9m0HZ/NLzF089/OLsKz/P+DvSa/290XVfe9/h3Ed97Quy67hzGJKTrupXneX8J4AMAfw/Av2ke9t8B+D+gT87fA/BPzW7hTzzPm3qe98i8zr0RM4FPTl7h0WffIPnpObqGQC4TiDzGaD4GoxLPrk8PQg/eF7KDIVQTjFiAjCdaiWJgd0eJ0sa0s9thaxsDoIlP54NVmrVfona+uM11lASyCjcWh/7zxtCVFZcMXPhYLMeI55VzuWga7dP2fH7s4PWfjIdv5oPOjdJVF/M6VxVGVLfQLMABMAsIFRqtuLWgkq1FfiielQxABp9IPJldb/gqCkk3DIv7idCS5W2i4dLTXL6Fno1eFyOcl6mbOx+nKyQRB28CVHUIoXQ7D98Ak+UI11dHmPNkwxV+XzCj+So73ZZeNHDoxYh2aDut+blqPSRseNM8iSqcThaYzG6NAlK9YT4tFMXz1QQjv3H+oNth54UnUaXb1bHme+oKSh9LZoTYAV3N8CZAFDSO9mKBbDYpW8DIEMoVWCfFk8URvs4Ph4tY8Ek/GbKwBqHMVYk2Gdpjq4W/kQyteMRd0TcCb5WHVgFTf1e71AaXnkOa+6biDlmLhyfXmD16hejBHJ0kqK8naEo9B7X2W00TOH1UqQhWRYr4Zgo/bMCLBKs8xdVygp8dfJZeP37gEeJ/A90u+WPdGMSfdF33n36fB/CDzhA9z/sEwL8M4P8G8NAmua7rzj3Ps4OXDwA86z3tufm/jYToed4/APAPAOCIrRGYERVI4grBbAUy8tAJBaB0nLs45sgCDaePmdBE5KB2Ir53hb1ZLML0pvawaBV8ok1Up4FuzQ6poTCrbGKS3Tbdop6PsLqZasUTX4ANeB1SqgYrpU50kEWkJdyktXGiLoECGpk6k2t0ECEK11e6irDIvqsqwXkZ47qhoB7AyAhPsu+OKGo7D5c8xCz0MPLXHE1q2qmy85z57T6xb8rEvXOiZyWDT7ROZ2pmp85mxyALc8FQCYp4iwM39SVmoXZgyAVzYKGLIsNFFYAS4NOscPSPNK4QBQ14E2BVJbi8nTpt0IgKh/a0M6Uh4I+dYWvBbKufCUfG970OK+XhigMx0/JplK6XLup12qzXyQHWCEal6zpEqaZD+ETpcxIOiFqbyumSh8h8TZ2waj6x3xh+oBYl6ANjlCLgZiG3Yc8NpcOcO3fc5nfjLNcVnKna+5uhpIfWtuR/d8w9Hq/+owAIUCZRFbvJsH+++u3u+8ImQtkZTijpkA481/5eJ0U9M7eVdpSW2spprIEydh5YLTNnDG45sa3RRBZSu27Y1vPVcoLz4l1hTN/aDPG7v3/XffaDvbmJHywhep6XAfgfAfxnXdctzY5g8KED/7fzrZnB7R8BwKfxadfneb1O6B10icgI/x4abWcFwCUqQczPFM9Wk41Zy2I5xnUxwiQq0Rh3dRrXsAqcHlWQVahVZySBqMONYbqSBI3hy0lJQBmwenUMGteI4msg8NA+y1CeHzvlGmvjtF1xjTJdmdrFzBLhb3mC2zrCognAFUEptJrJeRUZ4YHNXbXsPOSN9kPkwnctIjvby3vHz7fmXv3X6ANEyiZEfXOMugk2FkUbVRE7lZG8XhPmZadBKb9c+GjVMQAtkGC5hrOQg5F1u1coAmGAEAAwCxvHLa0ExZxrov4lD7ESxLk19INSiXGWg1GJeT5CYboNWciRhRy3fK1RG1HcK103FLLTSkna7QKDQBobSjAIQ4kBcJCUnzDtY0tKX382ZeaRCjGTzpmkn+g2lIHIeqZImXDXnPX+sx6HO9U/UThOctTCd/JmNpK40u+3WidFx28Vw8bAtkXuPoehNWT/P3vv8mtJkuYJ/dzN3Px5XvcRr4zIiuyqmqoG0bOYkWZBCwnBGhaI5cAgNkiMZj+0gAVCGgmJP2AkliDEAmmQQBqJZU+LETNioHums56ZWRmZcSPu47zcj7/MzFmYfXbcz+PeG6/Kquj8pFRkxL3Hjx8/7vbZ932/h6gH89Xda8B36Cf9ZBgxI133NC0GlWW/fWo2yZ593eH1p2s4Oskga4FilTnZwT4tpLUi4v2Nxtl4eS+U7ttH950mxN+F+E4Soud5AUwy/B+7rvtf7T+/olao53mPAby2//4CwLPey58C+Pa24/u2/YUjVcah6O8aj0mDvU1UiuHzmzNr1zOMphGoC2PiSm1TH6ZyNYlsuJDplkPbdmY/USjJcP2rZ5iWIVhco7g4G7xOKYaL+Qk+OTOs2X6iJ45f1dy+SLfa7HwXjbA0hf3KlGS4CF1XKYbLKrxTMYWSYd4GA2Se0j6uVhOEmwQPT68PXD+jP2laoPvv8fmKo9LneGZno3Gv3Wc0KQ/f/lPRYm1/RqLXBsiyD6YSokWUboy7eWEWyblt/aVhZXiU8caBeRyX8y2S4n1DK7PYYmXAPU0V7vEl3zS2+qt3PxcEeiJXEUo+qvPcIr/7fFFX4JBJNGMa2Sg3SdZKrL1NhLxFHBrfzaaHXB3GYTzBKOjwKJI4iyqXVN8ldGuwDIvV+N6v4Uw5MNGHCBEF+PRdQTV/9n7O5buK7wJl6gH4HwD8Zdd1/33vR/8bgP8YwD+wf/6j3r//Xc/z/mcYMM3yPvPD+1SIfiDBuJHomkUbJNak9j5BSiKB12EmWlxVAQL/+ML/5WqCLGhxZmc3NGOpisS1/3hSQeN2eHVbC8xXY9QycIr9FItvHxjx4h2JsqvF1FWqj06u91qt2SiHqIXZqR7j8nV2rtYE4F6HLPAHleLcVpQA3JyNKo5d/8i9z0TODC3feuLZyJsQv1icoJYBPn24JQ03VnZtVSYoJT860/ky91GpBD8ebRAxab3oajM7VcwqEe2jP0nTk6gGgddhFHSYBhpTC/ihZBimG+dqkVjhhSSqkMSlEWyXDPk6c61UqYxA+C6YJ2IKI86wttqjka/d7C5iW9eLUXD3Lp6Soq9802m4w90lDSuMqhhZIDAKGucVyGyLNQtaxFxiEpV3UydoHGDvz9AiOam6lIqB28/eb41Sa9C10pmGiGorwG21SxuxFQm4pfJ1qFf7O7Q5SawriVK+U2Eib8M+YnQ3zPfeYNoj8FMEvnLzZyLqB77xgGT+vquKbjmaPMH11QmKOjrYwt69nmRu/T4pJbtRVy2+/A6J+b8L8V1UiP8mgL8N4M89z/sX9t/+C5hE+L94nvefAvgNgP/Q/uz/gKFc/BKGdvGfvMmbrVu70M9H4Ge5A9WoyizeQWge/mOiu/0gV2ylgVFgUH9P0wqP4g0iluCmSRCxbeuOHLQrZWDyZ52HiCl8dvoas8lqe9xaOC1Ryh2H2lyd8tHWApsm3Jsl9He6SVy6h2e+HCNvIutft002qscpo4rTtW0UO6L2b8Ax65bbmRhHxCTWrUAuOdaSQWkjJu2oK15nHRCMk8OhIKTndcMAxPb6GVDLRRlh0frg/hTM1zifLJwWa583x7wOCe+wkZ4z2Vm3HSrdodU+RjzCzC48zNc4s1Jkd5HluWeEtLNAImI+nqYFHo7fr7wiXWvua5xFLYDAuU8YxKJvq2a287otOreWRridACUkE4haoGnEoEKUikH0KhzOjHvHk8kcAPBkMh+0qU9n5vOS9uehoDldyNvBvci4wigtcGp9KZ1J8oEq0WjOSuTrzIkvHGuJkqi3sJ+XcQWtfOsBysCYRhqXe16g/SAua1+nt9/+pHYpQBZWzM05+xUis5uqEdeDbgjNdgGgWGUI5wa7UF9PcH1xjq+vzx2ojQyDnVpN5yHwzTWL3sCm7F2j+75l+tuNruv+FIfnggDw7xz4/Q7Af/427yU7D3kj8PX1OZIvSjwKpJurUDBudl+8Oo4yJUDGso4wr4WhWrAOz5IaP5reYJatEa8meFlGuCg9VFpjI33XkquUZ1+jMIs2zhjYIEDf7ivI2wC/XJygkgEeT28GP9uUMTZlDM6UI82fJfkeNw8A1nmKeT5yC2arjUrLu4pYU8TccOOIdnAsKVJ8veGodIoRj7BoGC5rH6PAVJvLKnYk8qWd7QEEWDEUhdY38051x0aaWerB1SZzSiSHgjhlhI79wezqTp++d4mIKZxFximlD8Qx/x8Yw91ee5gcM5ZVPJzf9c5vVaRHhRh2F9snk/mekzzjEqezBa7n04PHWJXJdmNyoJUoRIuz6QLrIh38e3+WGKclRg9MW7z5wiTxsoyQjQ5r5i7KFFKbOWESVVCSQ0TCoaQZlxAAElVhtfO+AFyVSOR/YKubazZoQ6H1tfTBLHXoUbyB8retYM6UaccHEoARVed2rDBOC2TjHPkqw+WLxxCvz1CWEX756jHmdWQ6WU3ozoE6F+SQETWh6yb9NuL7GeJHHMagk2HRhPjNq0dgTGP2+PWA5kBtHaoSDymdSMVQyQDzOjTkW6/DWdTix9MbfHL+GuOTJULR4LNihK+LEa7qrU0UYAA3gddhKhqcjZcYn9+AJxWadQJZh25X/zaf78vVBFIxPJ7eDCpcqRjWGwPjj3jrpOv6D9Y6T/FycYJlHbm5nRGGvv222AWWBLZFGngd0GsbjwJpic924fA6cP/uW+6y8nEJfwB5L6XRQl3tKOZQcF+D+czQFGzC4J6HQ0Bylzh8jUfZ0vkvVoo5IWdq+5r2aeNEy9/UOeU+0U/G3N4nNKfdom+5bVUbBOOiYe61qvMAzXC92SoG0ebH0FoCN6clWD/97OAsb0f4gOJ0tnDdBAJgrcrEAY8AOIsjJXmPfG7QqseesfHJ0knegWmcVAIXXz3FqkgdcvZQ5LUx86Vjhk0AIVowbnR4g7AxSc+2RY8FzRT7Pqlr6TuhdcB0R4zLjaEOPWJDw1/Wa70zz7Tis7BCHFcIE9M2Lzcx5ssxvrh+4FxDpE1+1PInz81KMeRWdo95HUTQ2s/24RLjd40y/V2IjzYhSsszI9Rc2Qpcz6dQysf0/Abx+XzvNaSuIXYQpk0bWNSl9Q0UCs+zNT45u3T2QEK0eDpa4sebGCchwzRQAw857nU4S3KzkPRg13cpaihpbJa6hpvZQy1c67aUDIX0IbsRVOfh6ezaPdy0a4+DxumLbhF+GmUZuWS4tg7s/aqQFmnVg5sn3AhfZ0G7pwE7C2tETDnHiFu/m97iTO9l2lPUAoRDUo6CDqfCqKOQmSwl7rsi5gBgPAXbnvcgBW0gol6bLwgal4To90Mu3xlEQUHJqC+SfYy0fyyyoB1sNEhblZJiyCWCZmuy29dspc+stREH30V8OsEFpXDb8tBPhotGuM1D2QokbTDg3hLtB9g+Yw59eqQTQV0Akbe2/b+PNFad52aytQwctzKLaif3VxXJ4DU0l2yq0FWJ7j01Q8glnqXmmf4iD/f0ZRetjyzgqCXHadoa4QLZOkPr22K+GuM3yxNcVtHg+ZHYjlfouTYycfaa+Qni1QThAeeW9xlhFOD5u7pdfA+q+d0LbZPgtlLonGAwJbpOMusIwVzbkt2yO6aYCoUfjZf47MEFsvG2nUPggz+cLvCiyNz8h1CThMpclwluXj6ACBv3viIach61JRwXZYz5aozZfILxbImqSPCrl5/gS8uNu2+EgXlwtSX7isa4CNSS1DqGu05aoOdNgJvGtyazhig+Cw0nzSQxo+dJMQsrxFziN3nqjiP1vivDvAkwbwKMuGlTX9Yc67Y/ezE+gKOgw3lokH1kQRQGrdPdvG9SZJ7Z3V9VkdGjFdbqyrZDlTZcOxKS3tX2DHx1EKZ/KBoL+uCtGszhSJPzqkwHFfibJkNgK79GVd99o0+O71dpuxWbVAyyNLNcoubs/vy6GNkRQjgQDi9kgGplPAdnR5DL923/SWUUW8pNDCW54+oBJrEeGjcwy0kkUQJKIFIxYxRtQTqUFA9FyCWeZ2twr8PXmxDr1hugi6lqIzR6WYcHwTjviuylyO19k6xq1I3AD97LUfejrlp88R0q1fwuxEeZEDvAVYdAT3uQdsfKJsMempOkrvo7sN0HLmIaPxov8ez0EpPZcoB+Y9xKsYUVntrfpwWW/M8AI5P26vrUiSIfCi05yk2My/UEeRNiWSWYWLj5GydD3jpvOtJxbdpgMIPrB+ljLpoAi8Z3tkQRA1KuMQpapLxFpQyyU3UeUt66ZFXUEVrt4+UmPnj8fqwlw2U1XDRo4RlxjalQRkQ53iATFeKwRhqXUBYAVdQR8iMVBvN2VUaAi1IgYhme2++EwCQU42CDJDJtLsC0lNeb5E6EJoWSDFUj3DyPqh8lOZo2wPUmw/oOfdS74jwqXfuWfC3vo7LEvA7YWbQpEfr+YQL9y8UJNlWEh2dbykttlXmuysRRUnZDdR6+WU8hNcMsW/dQo9sNJ1VmtyFFycys89wAACAASURBVMKtKGNnUn1XkmFMI7QkeABgr20lal/nM+OXSKC13aRImqNcVFake4SXZYRF0+daasRB4zpJYdAeVeIhM20l+VslSKWBSvlYt4ExNX4Hmcm7o4N+A+/NjzE+yoR47ObsB/kFOukntk1sAKzxrhwkxci2e3Zh1BT9RYUErykZOuFjS1j/5uoc0zQfoE3duSmGdZHiosiwaASiKgLPR67F1ldWmYXA02ztgDV9jlX/fJhNiFT59HVGdxVUKsWMfqbN16MAeBKrgdFs4CsnDE4cMtJo7buH7F0jO2tsOw8XpXmP6U4hQTPKvhNJH6FIkPr7Vm0Ua+njsgoR8xE+GS3cJsG38HhhXQhoU0SfS1pUoQhaK9hw+H21rWguqxghlxg1AhnMoljUES6rGFdViCyQWxPZN2jFcttt6F+LwFdIeXuUdrIbu5WMVMYIV2vfzR2lYngxP8W6Fa6yokqxaQNc5BPXJh0ee/tZWu3jxXoCpX1M09wpBlFQUjx2Lc8fXro5pda+0fi8B9Brt6Xo7zwDd83giLTPfGM/RspMgRfaOXuHR/EGj2Y3rpN0Olu45/uiNBvNiG3Vlo6NRc6iCqrzjm6SlDatfijzTC56snMfIswM8f3PyH+f4qNMiIFv1CReFGnP6PNwaMXQ2NngbquU2jTLKnnrnX0tjSs7PRxkZDsJK+vtZ5RnmipEVAtDlbg8wavV1FEZKuVbBJuJvsfaaZIfJOsyXyNvQuTNxL0/ARAA3LqAttoiNTvTunwSK0u0N23HiClI25aeWpTeod1vq4ei1tzywiKmwbSHRzHAvHckjDPlQD13RR8MtDsTpNnWwfew1eTuor4bShqlEbmjMAJYOL02CkZS+/fmu7rztUITleKItY+6DSC12dRwphzfDiBUNHcbHtKKZV7n7Kr2zt0eEzBqMK1mGAUNHk9vBhvA/twXvetOiZ3mr7QZopkioVnJFYSOKUSLqogHSaO2c7+nn36D+eXJHjr1ja4bN1ZSWh9PvrvXgVtRCKV9BL6ytCrlgE7PTi8H9mMAMJuszD20OMGLfGRlII/cT16Hx0mBB9kKtQywXhjJxP78cNH4bn55IrqtQfEHAHVRmBni43c6xj/5J+/pZL6j+DgTIlf4ZDxHpRheFIdbgxRKMveQ7j4wTRNgvh7hqkwciftto7K0jVt/p0iglI+rxdQY1BIVojOE+JkwQIqUm9YsWckARsqsOTKzIRTgIXWZ24KS4dN0g8DXeLlJLLAocPqtUvt7M8hW+6i0f9BNnPsavLOO79A4j4xj/Ea++SztNiL1bgT+Vodyt4PAmcJtV4b52rRSe4auhwTbf1tBAI5thb8PigHgkuLu5sehTA9cO0LcRkzi4XhxLw4cPRdOYahXxfTVaej81mWCWbYegETaWmD5zUM3hhg//xYsrRC9WEP85slRysdd4VuQnLRgsvuE1GxwbeKgQWzNjeOwPtghYlxiOt2K3+dNaCUb99/zLKrwIFvhbDY34gAywIt8NEiG67aHcH2LOfPbhJkh3ioC9tHHR5kQPWYWsFHQgHnHZ1laGtTmemMSBmcKsTUcLTcG0EJQ9r6P3JtG2Qrb7jCO8Ud/zxrgks2QQ11qY0Uz4j64NVkdxRtMp0ukM/MQGkX8GOWB2d2iEcjbwNA+rNIIB8BUN/Ah3I0ToXEW1c4VHQB+k6f4esMd8pT5w2qTKiSlAfhDV/f+9TNTOh+q65DyrSgyxVnUYiqagyLKt0XEqKXr2T8NP/FYUNv0tnBUhEG77bt5dIhiUbZiUGUeSookZB735+Kd59DHwDApLsoUiyZExBQyq+yyy2ncDaNxKgebLWYRp/3vvk/TeFmMUMkAz04vB8cqixjpOEdyNkfwcA0v8RE1c4zyxD0bdM5R7/JTsnrboIRJnQ6qoOlPGnvQLJ7moCTZ14+kFsg2FWrJty3+nd/JRG02WKlRzclEBe5lqLSPQnpYNEApgSzo0z5+G0mxQ/d9y/TjDwdv31n4fC4hwgajZIP1JnG8JwBYFymU9jGJSoRcIm8MkqyW/GALp24E5rl50FXnQUmO2l7evqyZ1D6yoMWyjhD4ygEaiCqRN6H7/comlkqbCorav4S+y9eZ42hVhVk0dsOIawdYS4aImWoh461rB7WagXmds0TqR8QMJ+7TRxeI0g3UF2a4X0jf/fxpusHDdL2nYNJ2Hhg6B2iiCHyFiDFcVdHBqizwOzxNGjxL83vNgvde73UIeIdpYOY9JAV3ZTVV+wArIRqoI9zTfqzKBEUdYWTFDmRv5kagCZ8p5OsMyyq2PDIDpKHKvWyFax+32gO3CZve+003W+RDeaz1SnxSorhETA2+B0qogb9tbS6aEItG4FFcmiRg56XHKqstZ/L2cycCfd0a+yVj5jxC4Ct8Ys+fnClEuC9zpnvJCjCoaaZoXh3cS3JR2tGIsKpQZLlElf6udBx5PJIuLc2at0lOIj01Cj5NnhwVUCcJO3YLdYdAW7QhPI+6Aep60XgIPB+x/T4/WHj4HlTzXZ/AbyOk3RWP7cxu4A/IzYNPCDYS2yWZqhCG1B7xFpUMUEuOb5czZHZ43urhghoHDaS3bVNdVjHmA+I9c7vnl8XILUy15EYCzWp6Vncg0kip402CQBm8RyMI0brj9Nu0AJAFRpnlwU++QDDaQFu+V96emyoiaAfV47HoJzbOFEJIqM7DZRWBaW/QEnqaNHierZGF1WDmeZ8IrEwccfTI+4/7Chf5BC/sRoaIzoxpC+64+z2U9rEoMlyVqeFQ2o3NLNpgHG/g+xqX6wleblK02pDnr4sRfF+jrEN3bZ0D+4HdwNt2IG4LI8i9FWMnZDCAwaatUtwBZQhpeWsy9DVmYY1ZtHHz6foADeJYVIrhZTEyVk93zMWUZA4oRpUa0Yh2nTeOhdY+ijLeQ7n2g6TjGDfUDRLMmOejbdVoX5+dLIylXGDs2UhkYzfuS6TvJ7qUd0h5h0J6LjFe1j7aTrgOyIeI70E1H3FC3AM1WFWIQ4sfY4YyUdahAxbQ3IiOxVtDdF5WCWrJcVWmWLeBI9M+iku36MRBY73ldpOhCSLAkwA29zrnz1dI34lhB14HdVTlzgB0sJwiDas9ua3dCKySRhw0e9UcU9qpdLTadw7uP5os8PyHXyL+IwPamcxf4dHceLKRDN3uHCq1XMQR14iYdr6HtMhS1NKYqPYNdM9DhfOowmm6xjgtnPnurg/escgCaQxse4lqlFr4vUMBmpYyt1w0o3t5d9sUAL5YTfFFHrud/FRoPG4EplWCiElclIlb2KS1xAqKDGUrHPeQvltKSjSLpb9Htp193wh8c18eg+O7TRAljR4dieaL5DQyeN0dfFwzQpAuoVG1VrbC0TsqGE5fyFuH5KWQ9j0v8okT1j8Una3A6bmkKq2PEL5Nhca9n2LgMLKGYQ8pTEL3lATpT9/+W1JvNX4p0ska4WwNfmZFOey/a8kRpyXivEYmA4zSYnAsztStXQ8SdCdh+cjnYB7DojHfl/nzw1WIBlTz5J2O8X//6Xs6me8oPtqE2A9DIrfKF1YCibhITRViU27nbrSwHHtAASMYvmiEW8xnoZH26reO+o4K/UXfzAJs6xNAS4o1namU2s4DdIepUJiKxpjUVgFa38hJZZIj7i1+hQxwZkEL43RLoqb2Ei3QRpz6uPGuOTdSxtD4w+kCf+OP/hwn/9avceg2oc96aIf+ByeXTowgCyuXNI9VtMqeEhkFE/2h70BAZPc+n/JQEiCD53G8uXOTcN9oNcOXqwm+LEL8eg3IzpzwTPioVIzzMEAWtE4qkFCW5BMJbEULojrcChYAqHrapFkAjKwzxLoVYB5z4mCHUMFEC4nDGmHQYmW7FnTfAdvEdegzbTUz2cFW3KEqijZORE4/FoGvcJbkGMWbAYI35NKhlAEz364Uw7QVyMIK5SZGs04Q5gE8JdGuUiznEyzKFNN4XySgH1r7aGoBLbccY72zCZaKAY1AKBrESQnGlWmdSg4R1e9FGu3hw0uMi9ypWFH49t7Mm9A4oth2+tJunM3cffv7/e+8UmTB9uEqxLpq8OvPX3yw4/8+xF+JhNiPy/XEzHbK6Kh26X0j8DucRRUep4c9yshRgZRbyHj26PG8DsozajiPkw1S3iKWxmC30h0WjYdFE+KqCpByI2jNfY2Ut3tty1YzzOvI7fwzLm+d9fRVX55PbvDjH//aJMPg+C1yW7vqB7Mr46qwIwqwm8RoI6A6C4Bh0lgrhWa+R62yvI6Q1xGy+niSIyunSfTuyfC+ouvGkNhzyeU2OkvEJB4nJtlRZ6FShorBfHP+KW8RB80gqVHy7PMyAZN0RvEG4yyHUgyrMjEu63ZeTcn1TSkex2KVZ1jYlvFt/Enmda7K3wXlGG7u8LWVYriqYiP2oH3UjcCjOgQPa1y+eIxfvnpsr8Pdz+oqzwaV7epmsjfzpxkhWaUpydDUAk0VmoruHa+XzxTGJ0s3miEHDvr8eRvgZ9cP8Dofo1IcP1+NUEgPkd8ZdLb93um5oJAaqPT3LdMPGX/lEiIALKsEmyZ0s5+3Ce51eJQWOEty52dGmop9pNpZXKCU3Bnlpvz2G3qrzlIgETXiJrSCvzHWtl3ybekj8I0LROR3yHhsZpcWEEA8NPLyIxm5u0AqqvPwhw9e4g9++kuM/9btO0WaRe5GH4kHbKkspC7DW4W6NUa5xMtz19QmWBJ0VpLDL4ettqtN5oycdyNiyiH4+u+9G/dNEATkOLZparVNhtrMBCm5RzvWQISCpM+RNSHyJsS6FcgCibYLEHgGAJSFFULeIu2BXEgnFNrfChV4HRJRY5yZSqStDb+wUtzwHP3DJs5vG6s8Q1mHLuEHRzZDcdA4wYldSULAKCcd4o3KznPVYtmaNqUIWmeR9CafZb40OAClDCag7rWDhZ2NCiv+7XOJwCbEdZ5u26dvqBlKFenkk1fgWQk/kFCVQLtKUS0NMMdV9FyilBwvNym+LLZzR+oOEXhO6SG6VHYdqrtsXN4xOu/7hPjxhX3WDmldSosAzNsAsybEJCqdKDC9pl9JHZovBRZQ0E+G/aCFbxRvoLWPuEyhughri86k6g4AHiclIqZwUcYHHdzpAZoKhZvGx1UFvGxqBPAxDTjOIx8pDzELQ5zZ19QywNxyHncNd99XcKYQhzVmkxXWeeoquWPBmHJIPmDbclW3PH+MS+dpl9cRFhahmLcBzqISWVgZ8Mc9kHHjtEASVZitJvj04YWj19w3dluKxklj6xFZ2V7XiCs8TYt78yMjphD5DDFXg+oPMJXTvKFkaVqNTsh7JxhXeHRy7VDOu3QVOp9Dyd25kfgaETNiD/0Km5IhEfbfJu4Sse9HpTherabgTDlK0H3e99PnX6OtBV69OndJ0bct5TvPTxl3GG4rR76Dj+HMEPyf/hs/R/h0aLd2m6G3O/5Ox+GyCvFlwbFujRJU3/SZNon9zWJrq8NN92GeZ8DMED/76SfvdIx/cXn37/wux0eZEJUl/+atWRwuSgN5z8hy5Y6WDz18u+2eTRM6cv1ZXOwlw75aSxpWGGc5mkY4G5/dOIsqPJ/cIA5rRIsT/PKITmnEJKaiwYkwoJugGc52Fi3Dy02KcHGCiLd4sTbH6fsQkvQTsOVV9YNMgbX2oZWPTnbw+OH2X+Ab7tRsssLoZAER1bi+OrlX+5loLWEh75QtM16VLRJVIasrt5FxDiL+NrkeOtaukwNnCp89+fZg5XIoqOJ/UWR4WQp8uzF2UpQMd2PEFT4brXCa5AaNew8HBMB4Rka3AC4WrY+2CzATW9TobhgCeotnp5eQr7eOBY4msPMa1XsOiJLBrGXRKNm8tf9e2QrAenCmjRUysPfFpoocx/ZQkE5uHDQOqAMAyzoybdVNBqV9hLWVmNPMPYOPH7/C7F//NVRhns9Xr87d5yfZubKMUB0Rr7grRNDiyR/8BvHfrAAk0BebO1/zPkNqQOoPW73VVYNfff71B32P3/X4SBOi7/hgBFR5WQpMA+YQXIdC6uN+iORUAJhkOI4PPxBK+0hEjVFaIE6Nc302rxHzoZ0OJcMnDy4RpRtEooHqPHyxHg+Ox+1CxX3tqsSIsUHrRHXAZS0QFZmjdPTNZWleJbWPQgZAmWKKwiXFXTd1LTnQGO9sD3JvjhgxiVGyweTBlUHbrUySu746OXptKRjTiOPKkZHvE0I0SMMKORmqYtt+ukswmRbjOK6QTtbgYX2nByXt5hv7nV+UAl8XHuQt6xElw+dnrzHKCqNytBofFVGn4F7n/rvNVaOQHoDAtU8ddaJ3vzIuIYSRW+ujc/vH5Uyhsm14ABgFjQG62Dk0tbzfJcpWgJXJoGtAQuTrHh+Tgigy/fPY9fYk0+qrMnXt01ablvaj2Q1Of/Il/L9+BrYpcIovAQDL+QTZKEdCFm2bGGx1v3tuNx59coHx33qBzR//bXhtgfDrP4f/5RfQNx+uYgOoLf9B38LF9zPEjzUhdqYtyn2NyNfW+QK4qAy/byZaZAceeqWNS/WwZWpg3QtLs5iK5k6kJs3QaED/6cMLW3VOneLN09ESTx5cIp2s4TOFyWyJp43xOuxz25o22Op1+nrHhsYD8+jcgYsyctQDsp1CD4hRSu4WQt5ExjX+7BrrPHWLp9IG5t4pD17TAQeqROZ1CEUDn2n4gQRPKgSbGElcomnE0eqCIO7AthUcsQ7tPWTbHNx+p4q6j0+hEA2idAORmNmOz7TjmR1r5ZlNVYKXmwRfbwjUAnAYK6CIAVPRYcTNdT6LKiPiIBpXgYqgRSTbe7sc3Nb6bbWHQgKBF1jT4uP2QtRmltoYW+99NqtDOwtrJ7dGeq2HkiFnpqNQy8BIs90CDouDxiUqsuiSiugdDAFt2Oxcm+a+magGIvgUYdAi06YKXNhETrrCBDb6VFyApRX05AfwRAh+9hXS10bBKUxK8B2yf1OFR9GkdO/SPUIAHZGUwCQDsmfQqoQafQtveg1veXXwOLshohqJ8tG0AaIqthKCt7+GADWqM/NDAFAfNGF16PBhZ5S/6/FRJsSu2zpYjwLpJJEqtZ1ZUZKoZIDQumWXlhifHVhsqF1HtlKf7LQdjfP14cU1Hef4Ib7ZWsv4Cg9Pr10yBEzL6+z0xoEJ+hqSQbPfTuOeB+5vNToBM3Mo5VZzlSS7aNFjXudQpxFvcTab4/TTb5EuR1hvkoHWatf4gNDo1h2AFnoZ4fJXn+KL149MS4sp+L1q27dAhfuQ3AE4TlbEtOX1bVcHpdhR5Y9jIY/M1t42SMGmUluR5YjBJcNHkcQoMG1fo9hSO8snJdnW3uig6LkR5SbXdKAnjt3j2fWDJOgiu0C/7SzPKAVJZIF/MKn1OySMbS2sRNAibFt37oDlknL6f0uc50bMgj5H3QYWmWrHEF7nxOkjJp1tmAhapxFaN2JgTUVtUdV5mNeR63gAW05vP7wYCMYFwlrsJcOg93e6x3apGYybe9u33+Eh4J0nG3ibEvImRjMfobieuuOJloPFNdpVimazFS6PU2NdNqkjRJsUwIe0cnq70PhwKNbfh/goEyJF4BvVEtl5UDrAxjPw9j43q5bctbUWTYhcctt2PLzg5C1H3mZotY8fzq7u3V5Kxzk+Y9+6hXKXo8S4glYKD8+u9/hfYd2aXfye3Y5ZKAOvc4RvwCwS/ZkacST7i+go3uDs6QWSH7+EuFrj4Y3heh0KeZXh9b/8If7VV89RKY7sAJCIyMd3OaEPz19ZsXIPhfT37KLuC8RoteXR3YIIfdeImPlvFHR4HBtpuZBLV/3Q3Esq433XNOIgYZxQq4e4f7v3Uv/7Jt3Y+4ZUzKnHBGJ4XGbnkLcBf4iv148waN1YoVJGODyvI6eLmojaWUjRhs63z5KqYnf/RUw6rugoLZwPKZHiSYaQ5n2UFKmDI63IxrHwuAcWNXvJkJCjJN3W1sKimZnjvLrOTiDht9x1NPydUYvXVOiWG2y+eYZivp3915vYHfdQWz4b55g1Atl6goSHjnT/uxBhJPDDnz69+xdvib/8HlTzuxfMNxURNANsWyZi3CQQO6/pR94YOHneBihJ87O3mImgNW2dRgAIUCkfL4oEEZvhB7PbWyb0kAMYGJMeCqd52PtWlH1QAdg5osY0MMljKjBouwTWr43bNjHNZkjNBBKOlzabrBCeLuGfBOAw0H1a2PrRvhrj4i9+jJ+/eDYQAe9zvTy+rXJJi9IkhTuQpx5pnW4TOqkJ9ZOhUgyRaIxJcB3ZBXWblGfRxoFtpnHx3nh3FJQMAx+YBlZNJ8ndDFPq4f3SNMGtHNdKcVxVERYNQ8q1nS+b4Mzo20a8NS1lX5teLey9ayvS25KZsnQRZ/vUdgOQihP2PtIepdg9fxG0LtFCbSXfnBE227Zd+4C0pg3AvAgt4DoVCQlcW3I8GfcCVLH5g2vo+xrj2MjERVVsKsXO25e7EwbItJvA+tFvl5q2+XAZ9LmEx9Wtx/CX11AX0SAZUmjFsFllg2q0H6FoLIgKgxHIoaCOhLFS8xF1H66qrKoav/j8qw92/N+H+CgTIvclTpMcizI16v63kKx3FTsqvV+pcKZwmq5RS+7cKiahkRh729itfpRk7uHcNTl15+F1OA8bVGno3Cb6ke08wKTQY/5/+34RNzty7x52OMW357ieT3FhATuHEI6AmblQlQuYRYdxecB9w3wXVFndFn2bJcYUZqM1pGZ7oItxvMFPbHWSxuV2IbYVBrVgtfLh2/VE239bLIxQA7XFmLq/TZA7zx1QEreLuOpdf6bJNJlZA2bmaDgAkPFtAhC+RsjbIYeVyPk2KYZcopYBijKGEK39vMPFkgS+D3H4aNZH4Qye2XF6xu5npe+vlmbuTkIM7zvcd82MxOIo3iArMsyrBJmoscozNNdjxC9/A0gFde2hej0bHEPWwjrXD9vxVCFuE/nh7764niJ9+S38y/8XXrMBvr7A6tc/RL7KnL4p4xJNLVx3pNxstVP7UTcCqvNMZ8fRdzzAN+4zzNHGSCKQfs/Hh26zfu928REGCxTOpgswX2NZJYjVYdoDsH2oK8WOWqyQ1umDbGVg33bmcRf37j5BD6iS7F7qKNzXeJbU+HqzhfQz36AcM5tIybxXWiFnsvspW2vOytuju9d3jb4eZFsLlxDJc5IWt1puyflvEueTBTbVvrbpOC0Qx4fVaYoydl6GAKCtwPurV+dYW7mz3ST7plG2YqAWRDPlvuB1xM3M2vlcWicT1puJ9SPkLR6lprW+aIR1ydjSJABgUWTQ2nearYeiteAacrYgd4i6HZpXR6LZGt/25niHgjRQAYB5zBkPC/XuKNV+9OepzFJLiJ8aLqbImwhNG+D1zz7DJ6N/CVUJ1NdDLp2sBdY3U2PG3QjEceWAT3S/CtGgasTRzehqPkH8559iPPnH5u///Jk95nF95P7PSDQDMPSTvrNMq83mtr/+UOU4Fdp0fmxCpO/ww8T39k8fZUL0fYXxbOlaR8a6qRs4pvdjd57Dd4SIAfNg0qJLYJi2FqiKBOs8fevZFempHgsh2oOiwI/jxla1BhyRBa0D0QwfNt8lRfocIjAJkaUVAA/+SYB4nGMcbwygZ7KGJ7af33cI19tJ8ISq9blFcnKJuBbI1xk2VTTwHtya1OqB2/tu0O6aOIVKMSRR5aqpNC6RjXOENuH1q4CyjHC5nCKvI6RxaeH3DbTkmF+d4JdXD821s7q1QrW2ujtc5Ry6f6T1JixkgFgGAxI4zbwGv08bFXt5jewb9mTfODOu7RFvMQsrcF/jqoocKpPuT2U9BqVimI1X++dnN0VSMUS8HQjBk+B2LYOBz5+57scrReKsOj1WmFao0r5DRfeVijhTg3b8bsKkpDT4O9OD4wjRIko3CNMNeNQgyRNzvvMZlPYxX46R/uJTgwbthVY+NqvMiUfUbYCqETjjN1ulmrBBU4W3JvK6EZi/fIDg/yvQSYbV5QmU8t09Sddp9xjSEv77MocEQEq5xigwoC0jAbh9XeADI64xEy2412HdckvU/5Di3gI/+umzdzrGL76fIf5+hJEuM0hMaSHggKEiSPtwB3b2eB6VRmrryAOiJHNzwfs4p5NfHgUlDoL9b9uChxRIJKa9he6qigfOQRHTbq50LFrNwOzs8DRd4/GTC2TPXsHPWpBW//iPL/BHn1zCixt4QsOzfRuRGa3M6aIeLNhK+carruXopGlH7n5OAI4DJhUzlZ1dOPri6ZViCLz968i4vLVqvsvloLJu5JXiuFpMkY1z+FxifTPFF68f4Yt1hohpnCmGqaihtI+x3rjzLWSAtfQxCvaTodTMzRDJiLe1vn/9695v1zOvc4T4fqgOWLccM6vl2VjUc382aWbBGpOw2pOty60UnNI+RsnGnV9fhJu0Rd82CAFKZtp9pCdsa5iqZKkYEmbMtpVkRlwhqrCpIpc4xmkx4Aj2I7CyalEtsDowoxtcO3udtPZx8c0jjLICUbpBPM6hlY9yNUK+zrDeJE4sgVnw2MOHl46TSnNfJTm0tPd0y83ftQ8JKwX3lXGDYFwh5iWaKsQ6Fy7ZUvcIMPffukywrGLHsSTrteeTG4yCDKPAnAvxhP98EaDVlBB3rovXOePrDxF11eDn388QP/4ghGbgdVAamNdGH9K1FjUZ3hqPv0lUHpyF9FUu+k4ZtIARkKFuA9cqoVmhksztgnkPPNDavEDtFZOEh3NEETaYjlcO3Sc7D1WvXXtfH71MVHhwfoXx01fGukbsoBw/U0DboZMA7URZXCNOS5ymayyrxC1oqzxDPJ8gVj5kHbrPt53PSEPwxzYp8rVyrueASYrMAoBua532q0QKOo+6EYgPoFH7YsoAcJFPEL5sMVpluLg5xZf5CGvpOxoDYNqeZSuAFXBRJvgij+0xhslw10j5bWIXMXrdMKi1QS9PjxjeXrLxUQAAIABJREFUnsXFQd4lOShcbzKXRK/KFJdVhKloMIs2mI1XiJMS5SYeXEelfUM94q399+19XzVi0K405313S42zrVZoYJsfUbpBZlvoSjGXDKPZCrrlkJVw9wtgnhGRGUf61c1h4ErTCFdt0fySDJulRXg2VejuFdoEL5oQ1fUDR3Hqb2JI8o1QqPOl0UI9JP/mM2WMs9UW3VzUEYo6ArcOLfMqGfKae9fvNMkxiUo3azaV9xSfr76bZbn7nof4VyMhUjDfuFIb+bMhJDoLWkyFsXG6bZ5Ezts0j+gj4cpWGMFmGRycKwAAD2tnKqqVD1YLoPesNY2AEM0AaQqYpDgbr1DLAJWaOFQlVYZ3yaBlYYUnDy5x8oNvIR4sbAVo1U5++NdQ/+BvIDr/Y2hdQ774xwh/8c/gvXwFPzAtpXFauLmT1MaFYml3765a7iV9ANvWaSDBrbMAAKyK1F1fEbQ4syoku558wLZK7FfPtaU0FHUE39d7VaRW23ks2Ue1muGr+RlmZYKv15OBJddu/OViikXbA5yQ+EEvL9IGiDwF30csWh8oEkjtYxZWrj1N92S/TUogKapSDciGW4Umjks7Zx0FDc7GS8zOb8DDGlFaoipi5Gsze6xlgGUdDVC7ShnHiXWZIA0rY8dlN3F3KdkwXxsUZVo6IQTAoD51y5HkCepNjMAmPJ6ZFiezQtiA4RAGow08IcHiGj5TWN9M995LKuYMtqmSJoSym8vvdHBobp1rH7+an+Fxuh6oTmnbflXKd5vdXQrWrm9iWwtETYOyZxC8KFPj3tF5gGYIe32dJKqMgIMFQ9E5NhZsU+kZLsoDHRP/cNv+fcb3M8S/orGWzDmsk3rMfQWZ614y5Uy5hYXI+2VrEiVZywBbEjAPt5Bwn2mksyV8prCaTwaE5L6qCwVjyhqxbtukz8fLPfNd4p/1hcq5r/aO53EPSCJ0IkIXG2lw3w+h4xN02RgeXg2QqMK2g+gcq0YA84kDKVBipM+66xjg25kQLaiE7DwfLRHxFnkd4bMHF5idmfnO5haZrboNrJSbwri36Blw0v5CSCCQL1ZTI7Cgfexay1WK4y/mY/yrpYdKd3gU+TiPtr/0KJJ4ah1OANOWjHjrWpizbP3OgJLLmqHtjOzgWVQN5oX9GSz3lVuoQ96CaY3cumPQZslYYZVWVUjB732XJFpftkZrd1fbdlNFJrnKAI+nQzHr3eB20zGNC8zGK8zObwazPD8wNAbGFeK4hig3YHENFu3wBHvJ0xP74BaThLRJkjlZXYUILE2FQkk+4ACGojFqO5X5roIqxstNispeq7MqwaNs6a6B1sYlg9xOjgXd3/1NYGXbsvQd7KKy+xX39nk0aGohGvzo4Uu0mmHE00HnJ+bKOKp8YFCN7r6vED+66DofbX07Sg7Y8uBMMjQ37n2TYj8oSRx6bT8pUuiWDzhOZt7BoOaTLehE+XtV4m48TXM8f3AxUBRRijmn+Vqa5Py+KhjALC4JU2ga4VrI95mjAiZRUtLfTRyjeINHJ9eYnd8gmqzhB2bnTQtbWZqKR9jFjZJELQPTvrXuFU0VHj2fuxCtZN8kuw5Sa6hu+/uP4wbPs/VBUYI+UMV9Vts+zyUHt+osg/dyoBqgD5QwJrCeA8McC0qGJNTOlLF74p5VImLGYJpm4f3NiZIcRRnjdW7agw+ylTt/6nxQ9RtC3vkcBZYGMhut95Lhwd8fF/ei/PSDcQmfKfCkcqR7zoYOIVIxyDK24wd/q2NruY6iaJA0lUMDf11kbg4qNUNo28VvCpDrP9/919IogPsa6Am9121gDIKjCkk8vFaMKfz4wUvg9WNc7aCp4yNyc+8rwijEj3/6g3c6xld/+p5O5juKjzQhemhsQrzNVDTwO8frIrf2Nw1a2AkJ2hefPhRmTtJAtxw8qRypPVY+miq8txr/szTHZw8u8PhHZghOMxitGKoiRrmJUZQxwp7I87vGdLpElJbwmUK9iSGs4/d9o6lCW3kkmEQbiB7cP44rjE4WiGYr8Kw02pQtx/VvnmC+HDvlE8b0AOzU2vZtWezyHe8XBHXv0wh2g5Lhs9NLK44tXHtsNlpjMls6ZZLSXhOa974sBc5DefDYu1xSQhuq7u7kTUEgDqf1ajd4Iysm0W93+oE0ZrhNgEvriHKa5AOwDSEm72PGSxExifPRErOz25OhH0jw8QZebJJzV3LnTnGf8Lk098Z4A57Hg2esX8lVdrOWxiWEaJGdLOAzjTApUW9iR8/pt5bfNfrXrL+RIQWlCgCacJAwl1WCx7jZS4qhaPDZ6Wtk6wmurGsPxYdMinVV4+eff/HBjv/7EB9nQtSek87ivkLIJbKgtbqHwwf9rtnbbdFvf/RJzXdFPxlS24hHRhSa5/cFyNSI4wrB2CxmNKORlUBgOY1kykutoCBsXAJ+0zBzTWVmoEy7Gc0bJcQmwLIymqmBr5xmazbKEaVGhNnjytBB7OwRMFy7sG4xG+0LIZBF0yrPEIlmz7KLgqgptFi12gO4j1GwFUIfBQ0ex40BXSgfj+IO00DhUWy8F5O43EMCx3HlBKTNtVFgRYL5eoTLKsLXhYdKBYiYwlm8cSjbtvNMdegDJ1yD+cBF6TvT4duCgDMhbwf3HXUoSJowE5W5JjvKLFcLI7RwFpWIwxpCNA7URVVVyFtETDoFGgpSDMqarVnwaZIjsqbOPDHJhhDIg+8gauDFEp7bu0gwVPdKitQuNe1U0zadzJY4mxuHld05IFFyRFSDR42b2Qe9WWjKW1zCvDf3lWvhC1+7Y7xpHKrqKSnmllpDmyPa9JyrJcaZacNTtyeNS0SiQbSc4mqT4aqKDnJV32d8D6r5SBOilAzz9QhSM4ys3JO07YtWG9WPwOssB8yKYb8HMjE9QBdlgmw5hRCN251TkGKK3Fh3idJUGtVyhHyVHWwnUixWY1xvMlxWESYHWne65chvpsjX2eBhJuHkIGxcktElA0rA36zA8XMAQPkshycrRL/+v+D/4hfQ6w4yj+1MziRAWYfwmWkDUxXO7M92KRe7oRSzs03LQbS/n68zKMlNS0yaBZIqxKYJHIfytgWqv4Ado8sEvkLU6wQYIffWydlxmMqb+J1T0SILjI7ssooxyjNwptC0AYo6GhhLUzCuIKLabUKkBip7OhFvoax3I0Hop6LD86y0G7PUgSkoKdaSO3J/v4PBmXItvt2gjkcYtBA780MluZ13M2d6G6eGPtA0WyQvZwpZWCENK0e5AMzGaJRsHLqT+RqjZGM2JpsYPt+ii7ViZpNgN4D3CVkJk/Rs8uvkYaF3FjWIx2t8cnaJtUUuE1qUNkl1I3CX2RPJ4DFfDwT1pWLObYaCrg0hUFHEUJJjvhxjUWSHDcnpnGC+l12njlryowIfjBnTZ5Lcuyjfrgty7+gA3X0PqvnoQne+Mw9NrUt7bZ3EpdUsrZQlrFt3iJjLe83abktYgLnRF00ALE6gtI9Pzi6dSoqZNVjBYgKgWDX81XyC+Wp8VPrq9eUZvl3O8HWRYd4Y0Ex/VqZbjnKV4er6ZF8kminESWk0GncqHHUTw8tLsOt/juTyG0C2wFdXUHkAVUSo5mM3x2tqMVChaRrheGmsR64+Zq3TDyIqM6YMojE3ixrpSAJmcVTq/aA4yWKIiOiBH4NbRxTiiCntQ3oMn41WqJSRPAv5VgT+cj3Z4wDuBmly+j0EMPNg38dYKNG/Rwz4LCvxfDJ3i16lMuv+sR9XVYyz6Pb5HEUcNBBBa8FZBzRLLRBG0EaJKQAJynKrHUoV6G6EosE0zQcqMlIxLOcTJzJhSOsaIhLwmUKofLC4NjzX+PiiqyVHszb3lMcVmnVyVOSdRw3GsyXqRryTahTzOiRRtVXpgZHw48rwG5U2xtl9jV2SFfzm8sFBi61D4ZIjOZzcc0RzNl2Y9+y899biPRbfo0w/wqBevghaB20OeYuQBxgFRvlh0QiXFCtlCNTM6+5tq7PLz+pH23m4qkJIfYJWMzx/cDGQDiMkJHEZyzIa8PN24+r6ZJAMjZUVH1A7mjzBV189w2+WJ/h0coMkqgYiy2bR20mGq8RpPgbXBcTNFbqGo12fOmIy6asSR9J4yRmN0lWRYlGmveSmIaLatlabg+LIIZfGM9IfCkBLxTBfjl2LCzCLo1TMzHfv2dreKqSYHX/TBsiE78SkAaNI0z8fohxwf2s6nGFLdTDzqQi1PR8C1kTW+3AXTQsAs8kKj+cFzqIIT2KFRzaBAIbik/IQU6HwKN44dGreRDivIny92V9gL6sYLwrjHP803Sez9yOwCjShaA5KkSWixihojTKRa/Xyrai2rUT6Qt3b62u+h3GWOyQ04wplEWNTxk61SWvfiJSXjZvZiXIDPt4Adyy6lBR9piHr0Ak+9O8nT0hHCTo9u7EVrtF2hWJHjZYp0rhEVlaYyQDTuMAoKyCiek9CkdR8KOhnZRnh6+tzLOvD3RoKMuymuG/bk9Yt+v5GjcCZDG7VZX7XCCOBv/aT5+90jG//7J++9Ws9z/tvAPz7MDfIawB/p+u6b9/phN4wPsqE2GoPeR1hlh0W3ybgAQDrd+cNblSy6DlEBD+0SByKSnm4KAVkZ/hTz04vkY3ywW7XqFykA23OXfWVchNjXSZYtwKlZFB2xtRqf3B+i8sTvM7HeLlJEDGJJ2zfU4/4j36soPMAMo/RkF3NJkY9H5lz6D10tEgCJJ+mLS/QSNkRH29TRWYWJRmC0CAJKama4xiZq0xUxn1jpxIu6xBSM4yLGLFtm9H1joPGGdjeJ0SP80jfG20Q6JicHXan5+QMshOBr1DDuEfE2sfZeIlslCMer/c2GoAxhP3Xnn2FSnGcxcVgxpWJGo8TY5Z7CLVK0WofEQPmdYSXZYTL2gfzBSIWO43Tu6Jf1fvKN/D+oMWnkxv3cxJWMElFuITm2oaN6Ikj+G7j03eooHnyqkgdT5L7yt3PjEvITQTRcAA7iNtKQJUhmjxx/6Ylh7a/tivtdijMRsyOA+w9eVvESYlP2GuEvMWTR6+QWBUjWYeoihiGCjGUGZS9Z+FyOcVVmViN2eNiChSkREOt8MA35t2VMi3svrE26bX2g9sOQ/YBxNMp6qrGzz7/9Qc7/j3iv+u67r8EAM/z/h6A/wrAf/bbPIGPMiHeFszr0GKo7nIIxOBaJBZ1R44InJk2E2N6AO0+Fi9LAalPIBXDj9g3w0qxz5/bcUy4P1Tl/uEH0rSsEh+eshY3dh6oZAxWC6cJ2g+6Bmbg71ukp4YQDWajNebrEerWPNQibCCSEuJ0BVVEaHaSLLulFXcomF0IdhPooTDfjdVptQtooobzyv7vjpINNlU0uPZxWCO1qL/CErPduXgdGJPGxy8rkJ0sjp4LWWH99WdfuhkXReAbWy6DSr59oXdejzaUNnB+Mue9S75OKYZ8lTnRhFVupmqk0LLKM3cvOyJ6v/PQBu49qKVIWr79LoBWDOUmHpDk74qu8aGKCOXl7Ojv9A20b7NjAuC+97626G0hwsYlQ5EZbiQvm0GVSBzjQ5ukQ3EXUl113uB3SiumEPLWadGS8Hh/88ws1SgT+4o57ys6APo7bJl2XdcX402B375b8UeZED0MVfJ3g7hL27ps+PBIzcCUdhQIWhD6AsjkW3gf37/LmgOrCThT+OHjbxDzw3MgqQ10u9/S658z+QYazVV9Z5XaD8YVPK6MaHcQwIvbvQWG5j/hTnu3boQT5959TwJZbKoImypCNsoRjAv4WQsvluiUj3aVoq2Fcw3R2neI0DvPu8c5vOv3GTOAFhJtpvMHDhsWM6YQisYlviSqnBYm/XxTxoMKPgsrjNIC6cR0Hw5Vh+743CToKVs53iYHbQqkA3Ps3qeGfmEqCtXbOLTatOMrS+nI3kBCjlxH0rh0vDxq11ObkyQId9vTpEMrLACHFGhYXG8TYssRJ+Ve8j8aTYeuFNi8PEW9iQ9uxPpBs+VDhH2agRKgJ74HfYkq2zDdQIyMYg51UOKWO3H4ug1wVaaYhBXCoN2jSNwnuAX1GRCfB+Z7ADS4byrHdSsQVomzL9sq4WwVdwBY3uWHRIG+F7eLM8/z/lnv7/+w67p/eN8Xe5733wL4jwAsAfzb73oybxofZULkvhEzPrSAEuw5sCoS3P43smhDuuFokE6RRNXe8fp+fdpCqiOmEPkaxc5itWgYXuQj4OUn+OHjbyDCxu1ka2kWIpKhCtvAtanIsaO1BN+Z0BhxH2fxYaHmt6GRUFKvG4HQJndaoJraJMNNE7rqmILaaLSgXxcjTMcrdJKhUx66kjsqCACkY2NEXNyiQKMUM+2ydv/W3NXavE8wrsCkPlrJ31bh735HVP0QuIKHphXcT4o+l/C5hJLb9t+h9+i7ftD/06IJ9EXojfA8Efkr5aHVxuMzb0KEfKse5LwXbQXSdwghXdI+YpSChKmJzkFV/DgtnFuD0tY6Kd0mhE4yaMBRGqiqOsRhbGphBOBbDtZ00CXD5ptz3Lx8gE0ZY5QVrvLcDfq3TjJ0DQeYRteYe+Q2w+1+HLqfjv6u2rZIF2WKeR2i1f5A3u5NgltTciPLZvwOA48EAXxIr8Oyjozq0YHv57cVURTiJz99/k7HeP1n/89V13V/89jPPc/7PwE8OvCjP+m67h91XfcnAP7E87y/D+DvAviv3+mE3jA+yoQY+NIMwxk5UmwdxAnyT6apzDOznLTHt6KWD1Pa2SVloxyMKyznE4fEqyy6rZYBrjaZI9COAolKC7eIBV6HqTDHpqQY8dZJrPVDSY7X+di0aYoWmybEy2KERW/HexZVRhibKbSrFH4gHR/wvsi19xn9jcLPXnwKpRhO5+PB7pYitBY++c0UTT3cxed1hKvFFIwpxJsYZRHjaj4bCDhT4h4Y1fZmvrvvZ3bbEuxAe7vfDq9lcLAyF8KIXjdtgMqKNZevzSbh8eNXg+qEgkeNc1EAMKiaSA5MdR4u8hEilmC0MRuEL9ZjfFlwVApIpEFDE2r5pvGxaDosGuOMUUjfengy5y15VW6TMLXhBtXnPTsK3FfOjYKSHKGJKfo8Q1kLLC5PcDWf4boYud/JwmpADbl5+QD5zRTZywW05Hj9zSOsihRJVJnxwwHqjs+l4xFW8zGq+Rg8bCBrgc3K0HWidHMwkTZtgNw61xMyurUz1dtCS46mCjHPR5aTyvZmd8blhKGUDBk/XKmT6Efga8idVilgkuFdc3ElTSt6XaRYlcm9QX9vE1VV4/O//NUHOz4AdF33797zV/8nAP87vk+I7x6e1+37kmnmvOkIag8AbFfQshdKGwCCaaOV4KFBoeXrDKsixbJKUEuOdSsckizmEtw6OCwac/NOhZHRAkyL5EVuFg2SdsqCFuc7cPplFQOIsW6FS4aZFRiYRZst3L0SQDWUqXsbxZ23CTf4b4LtZqLz8LNvn+J5I3D+cGuOFqYbhLM1gpFpR/KoQXE93asW8yZCuBo7gMbGSl6RLRIwVAW5LWg+SlUisK3WdpNhXkdIw2ow16WFU4gGohEIGgJicXw1P4NUDE8emaQYYLt4s7iGaDmqInZ0kn60mmFeR7iqQjCvw5VvWrK/WAdY23V3Iz0sGoaUe1hLH4UEFq3EpmuRS4FKcVQ6tO3T7bWg2XirGZZVgkxU95q/9iMOjehDYiUFgeMc0GYTY3UzwdV8htf5GJdVjFHQmvmonb31Z+JNLXDz8gEWK+MiQRxhpZjj91E7k64nSb3xTQRZC5SrDKv5xH2XIqoNEnUHiCKVafdXb6BiRMjq+XLsNrmHMAatNte90ofNnYEteM+Iyxt7OfYGDCKtDNe3KOMPngxNfLcGwZ7n/bjrul/Yv/57AD7/bZ/DR5kQgSGIQirjGL5ugzci4fcXXSI3M26ku66LEa6qGLnkmDcBZqJFxqVJRrYNCxgBcWDbyqzaAJe1wKIxC2/gd0ilSYxnUeVatuvWCA/3OUvEHQO2wtjSSohRy+y+VlD9YFwCR+agcVriDHBtrWOO4v1oNcPnr55gvUnw8PQa0/MbiNHGKJXYGRCLzaxvV/lFKoZVmaCoI9e+S3tITEqK3N+azobEIbylfUYzX8DM01wytJVfpYa2XQeP4Ru90MLeS9X8DEr7ePLg0sx8uBzMZZXkzoevDwwiEe7LmjlTWOOJCFR2g1Yp015rW4ZF42HeaFyjwIotcdMJzMsRKpWi1WbBHgVyr4qhjcMxAv+xz8jZVniaKq/ZZOXAHhSUmK4WUysYEeNlaaghU9EgsCIACbbf32I1xnUxcvSV/ryyqUKIqHZJ0e+pFQGmNduuMly+Oh+Y7oqisSCf/c8jldnAEhCONFGPRScZqiLBojDJkIT/H6drxwd0x225pUAdvmd2hb370XaewzAcS6hkL7f+rSRDg2Dpvlti/j/wPO8nMLSLr/BbRpgCH21C9JwZKSmL1NYI+FZotK0a4qBxi/G6TNwCEYQN8lWGdZk4X0KiQpjWyTBZtJ3nEiJFpZhFCpKOpYdF40FpAWlnkNSKId3CfuuForFzRtr9bhPFFqTRt6ZiS23Qn+UK3bKBLplTyQG2zuYAHEeSIk5LxFbDlOJQOxTY7py3x9UOwXcXSrAfdP1H8QaRaFwCc8e1c643CTKeBeDaoFIfRyQS2vDV9SmuN5kh94eVFXTgphWmTRs3sjPFweuVoR/MK9NujJgRf7goY3y9CQZO6ZUCcqlRKY2Mk3+fB2aNmmUHKCur1XoNGk9CdkZtiQQnpPbdZi9iEtO4GFSHTRugaQOsi3RPhYWikgHWm8RyGLfC9AQiam2FxrhCscqwLlJIzZytEgBnuF0pjqoYoZIBsk1lRgtlilb7GNlnbGDbpNStikd+YDiPSVy6zQxn6ugmhmbzWvsODEednrow38mheXW/S8B9jcfpGo9mN3sYAqN/PBxT9EUkWvu+278bUA1ZOBn6lHnv+c74gDGF+WrsxgVkYfZho0P3HbpddF33H3xnb27jI02I22TAmcKql8DuikUTopDBALRC85NQNJivxvdWpniTWEsf1UbgVOzQA2wy3KqeWMCE5f65OammitNUkbu0hvlyjCjdILgeQ2AFuUogN9FBUIKSfGuwa4nbFH1ZLkAcVREBTNJi3KAR39jdwCbDcZYbcIvkKMvIIT535713xa4/X1OLO5GISnK8ujLJEIAj6mdi64N4F23CHavzUMgAl1WEFxvhKkPzs20yfB9ByXCUmPY08Qr7sSgyJDsQfqqy8yYCW41dEkkna6SPrgAAxcWZm8Wt8uzWqpxiWUdY1pEjle8Cv4jzyO44lme1dEXYYJQW8Mv4TtqJ1AyyZhhZAfP+PVAXibv/O+UDO4/1LKwwiUqcTxZ7ydAgPo+PJmTnDRZXqX1U2seiAVLuIfI7oHfqhfTx89UIpeSoZHBQEelDJ8UoCvHTn/7wnY7xZ//0Z+/pbL6b+GgTIjA0cQXMfIXbthdJdQHb+chVFeOijFzSeZiaXbHUBmlHaEupbpcTY16HjEuMOHei0veJjfSwkRznkUZkkyAtHjGXiJh0bUI6b/JmPDZTk4phXRqUYDqfILSUgs4msj652p0/l9CKGRPXpHRC5J1kNon67rWAITn3uV9Rjwx/Hxm3PpSd3Bv69AfAJOYo3SAuEqzz1FU3fdPcY2HaabX7HLzlRqGlPH5uWhnlnF/Nz1AphvOoRGhdJYz0mwJ611xbdOyhUJ2HdRtgXgtcNwwbub13KgXMG41cKnDv+D0V+R4yGYNpBtFxRF6AjPtI+P6iHAcNQqtfCwC8Ny+ViuGb1QxXVYTHSYEH2QoiaFHWIVrNUMgAUedh04RY5ZlplSalm/2KVYm2Fs5g+D4bEvrsMVd7XRQKqYxUGgkJ3IYeZXzrq9nc4/2V9lGUsZEvJHGKliMe55C1OOjNCBgBhTSsDgrGn6ZrnJUJgMj4VoaV8ahU+5zTTNSY1xEK6eGy1rZC3n7Xi5bh68JD4ANKx5Cdh6loMIs2W8qRU0z6cFFVNf7y81/c/YsfcXy0CZHUMugmSu2OK2LScMniDbT2HSqOJNzW0gh/r9sAk95OjTiCFMxCqY/tErOgddji+1AhKgVXNRTSQ9QrXkZWmb+fDCkoMRx7WAgdWUtuvAjrEEKaxc0PJHzl2515j06xk8QOOWRoNeRIZaPcgE/ydrBrz1cZUouE7e/ASbSZ2lNJXOLUGgNHkzVYXKO8nA2MXhlXxiIq3eDlt4/A8ebedf3Yfe2XyxNIzZw25s+uHuIvFhlOe6CoY9FUIZoqdPOvzSrD1XyGV8UIrfaRtwEK6e8lw3XbIZcK664Gs/6LXEe2tQYAnhMHfyJiVNpUyBnz8STpTKVxz89KyfBFkaLVHmYWZJbGpTHFte1M5pkWaFmH4HmK6P9v71xjLMvOs/ysfd/nUtfunq6Znsz0eMaZdgbwTGwzdlAUTGI75hIighSDEgMmFgjIBQiKBSjwA8kIQ0gQWHGMQSCUIEyAYEWxLOMf/CBjz5DgGbvH45n0jLtn+lbddTmXvc++LX6stXbtc+pUVVdXVVdX1Xqko+pzatfufTvrW5fve9/1Ti3Onax3WFuZvWObsqTw6nM3ncitruWkIEUxCihGAU6v0DJuKrO03+vUFlzbJQyZznBRuoSjUKnypAFOY4p05rE3tz72PCD0clpm2aERFLvtAW8rl5kbdFls92olpGbynu9ULLb6zHd79LMQaJGWJX2hFLLwVbuzmgn6RYUn1LqxSZTqZ2FtED3ZeT8Q5KGvIR46xzYgTiNyC2ajhG48pB1vjGrW0taYfmGu12OK0qWccKPfDRuWU3s55pK2l9cSX/vdS/TqnnEIZFv2zB2/INdrjhvyVuOYtUZj0ptlAaMsYP3mAgutFOFWuEEz6cQ0NBXzSzcIF9fw5vu4Z13k3AzR1ddILp5muDyP45a1V2KZBlSly/Xrp2s7pb0yisWSAAAZpUlEQVRiGusrvVmu9GZJS5evr7QoJSxOafvNlGlRuWP1kemgxWC9w/Vbi7zZm9vy/0tLVBlFXtCTWplEZ/jdLIZAC3DqoOkJQexBF6HFwrnjjEUzZXqjP8O1JNbZq2qmpBWl9QjfG2xkCpuGtzdsUbx1tn4ubi0vHGyjPIWq8Ojf7rC6OsswjVRN6C4L1IvKZZjExOtd2vNrALTPX8Xp5FSJi0w23+RB4eNlEe6gYqY92DRKnGkP6mJ6I3ZQlC79UYQn1LLLA4u3aM/0OTNo4y4vMJQ5Xjnde9MTqjTDGJZPZovnlctqNiVzaN843CzT+4ETExCNvU/obTzApRZrHukRVOSWdL2KXAo8p9JF/LrRdio8RxVme26JLwWxp3pzrqMSYMxIsKkmYeb9tzOgBdW4TfZ1Tdp27Gd1AokZ+W69n+kjhu1KMRy30uuE6stmGr9pyQ1Fw8Ue1KhtshYvbCV1Cv3ayiyrq7O4XsncI28pr0Ooi6pdt2L2zDKth27ink3h4bPkS+cp5h4kWHyZmG/ARTVKDRbXcTo5buLSXm8zn4ZcX15Ux1GLWZdTxbabVOX4ffDckkgK8kqd15VBi8tDU+S+7a420eu3ubq6wNpoa1eCacEQIBMFK84yOSMoHgFaRI4gcgVdXwVBYNfp+0Xpcn19jqvDNsupGqbHnjK0NrZgwSjDd8q6FKjdWMNKRqHqfGzDdgoqKlPTIXKrO86CbirPrN5c4PqtRVaTNqtZqDOyE+biwR1LAAK1kXQ80yM+s4J7poTZGdxhilxLlCXayvjf9LMQ39nQL94UFDt94nZSC1m0tHfmoPCZiYdKjGJxlZnlBSJXkogMKijlRtMbuRA5Dh0fup5aKjHfV9MZzyuXtVHE8gG6Xag1xMf3tI/nvnq0DYZPTECEDZ+0gVbzL0slSZXqwmaAs3GqlWuy+m98p6QdpvWXL882FEFORSmRW9616G7kSnwhSXW2oNrv7vfj6mSavHLrL5GRr7tbuadpo0Xj0AFqrVFlBbqbtmkm2/SGLbKrPo5bMq9HpFm/RTKIN5Vd7IZmNuD8zDqdGSW2PXn8Zn2vyj0cdOH1KKindiMvJ/JyQq/g2kAl0ESNU4q9sk5UaprxluXG16cslS7ozd4sV/rdsZR9NQXmkOp6NpVRWk4NhqvVW5QyZ9nt4BYOZ/wIJUa4GV8oKb+dSEYhq1mobMlQz9xckJGWXr22Nkxi+lnIyiig629YoZnO1+qgU3cKa6/HiXpQ4/NoxPJzXaPnORWnGiUbhZ7KzLcoCTHB0HEr+rfV1PO1/iyrWVBbt3laGD3S5sZmqtpMw08m26R6VDlMYoLbcwTdIVVvHYc+spD1KLHKlai3aQ9KKVhJW/XU60x7ukJUE6MsNfVeCBU407KlOsn6Oi+14HRYclY7g5iZJU8n8K2NIm6mUV3bfBAk6YiLL79yYPs/CpyYgGimedbSWKl49ApVn1h6Y6O3+Qn3AaMy35RuKyp3TGFmPkx3nf3lCmh5ksiVRE4FhUOuI+Fkqcad71OCU445Qxj7mrxSGbc7mfgapgW6rchHAVW5/b6vr89RvLFRznH9+mmqymFxfmuB7K0PzlFSYY3knZn5tTphqIkJ4GXhjq3vDZO4fibaYVrLn20Q0SscSkndY590ycgrh7Tw68xXU95jMGtB11JvbO1wv9mNEEPbq5jXSVRp6XLp1hl6SYu1NOaVtVnSyqG7xd+aJLVR4U9dRjDXyazHG7p+zmyYKk/JVCWNmGlZoxsc1Hq1qtbXqNO4XsNzUbtFuEJp+YaeMkDOMpXkk42Uf2azXMGg7ou6T0WpymkWcw9/ZkCVe3WSWbrWVWvtE53BfhZqs2af07NbP7OlXvtbzQIGo+lZ3IlI6OWz9fMF48HQnGtfrz17TsVqFnB1yrTu/iLBriGeHJpToL0k2OiF6UzQ+XaPOW2rk+U+wyzEdapaucMUK2e5jysiwobprGkokjzYNgO1Lth3oO1Juo2pVkBpHWoB77uh2Tg2p3d2O0rcyPa7szVL4/E4mbHa5NagS/mdc7tWTplGpT0aPbdkbm6NeGbDDql5zMqWyIfMpyy8OkPSNHjznR6teGPt0+A5Ff3cZzXz69rOetpMr+8UWg5woKdHh1k4Vo+Xlg6Xh2494vedjcSp/aLZeTKBxoxmJnGFZD7MVNG8U6o6wdLlSm+Wq8OY1dwlcnd+7szobnppgKQsBf1cLUHMBRlnO2t0W0P9vZFcS1okhTJgNkG20jWUpnDe1K3WdaNy3KKtXv9sJ7huRZJEtbrRZDA0KD9LVTep3Gwc2jP9MWWcdBBPrc80572sS3CMh+Wm/yMLWBlFXOqHPNyOp9fqioy0kqSlQ+RKzrZyTkXjHfGmmfBqGimDAO6+s3zn2IB4okhLl+XG1IORVfO3SJ7ZKYmlaExRgkrHHhUqs7NulCam1yJXkks59nDHXqnWIQ/ggd9NAbsZURlt1J1cCGDDNLXUAs/GK29sG91Qv762QOSWzEfDMY/AaYgp2qLN49y5jlCl8ZvSlJEWuDZJJvuRoJTrTObBKGrUJlabkn0WgorIlaSlYFA4eI6zq7bHFTAXVKSlqEcVZrq06xd1SdF269STRG5B5Crz4bTxd54O+KGfE5LX+qugit3Nc93XIhZbJZ01R4nmuhtMcJuJhwT+uObqtDXgWuR8iiYoUIuQ97Oo/i6bF7rEqp+FKgPUrO+uw3q/M+4AMgq2LKtS07WuFtdQnpiTT+Awjbg8jLnUr3ii2+Ydo83PaElOUVX0cmdsat5glmtupjEro4DVXLdVfkl3F+IWd4PNMj3BuI768sdeQegVUzPX+llIkqs0fM8tGYyiupdI6dWp0aBGYQ+cukWW+SyvztHP1BevLLzah9FMK0Wu3PTFPhsndIIR/SxkOY202oerU7bH2SrLrpRCSYXpXq7p7baiVKn331xgQWtE7gXlBRlMFcw2dlijLGCl36U/kWCiRNCV2IGji/e9KCPIe3jJNfz1dbzWK3D1Vp1lClAmIf7MgHy9zcrNBZJRqES5ddJOfQxaYcYU8jfX/iYxogvG+7KXtDYJru8Gc089p6LjVDzaVu9jT60nFpXD7Syil289i1BWI1K3TyJmKWSE70DXl8wHOUnh1g0kQKcRDO+W01GCJyTXkkgllAlJ7GcEeiTvONVG8bxTEfsZSa4aejNanPRtNNTByUw310o6qj7PFMzv5Cu6Fzy9jJBX7pg3sXkuBkmsnF60Fdhaqgy51fFPSAvq6WDTJoTaFHuSQqoOwXq/w8xaV+UqlIJcGE3j6R3frp8xq3WNe3lQzxR1vYqlVlIHy4MgikKevPDEnvbxta9d3aejORxOdED0dYpz0/ZpGqUU3OjPAExdK7zS73Kuo+SdZs8sU2lHeXdlXhXyC3dLNUmTSXqu02Np7jatOGG936GjkzOMBJaby6l1iNPIK1fZ+ehGJmhMUQ6TGPfGKboLqwSd7UdoO5FlASu9LvPdzVZbgyRmddCpG85JTKAPvZx+r4N/K6NIA4J+TLDWQ8QrZDcWSFdmNv5mrUvWbzEaxgySuJYMW+l3VcNUbghRN4W7AbrxcGpQNKILZvtmgtXdYux+AObDrE6uidyCtPQ43dCy3YqSnEQkFFUHVzjM+WomI3KVQtCgcIj0s2MSunbKZJ4k9Ip6yt906q4lEZ5en5ss7DdJZaPCr4NibWOlg+E0Ieymj6bvqFmQyC2Za/eZ0SUQ+SjY5H6yH2xy/NDriGadPQwy0GuGhX5m+zqTdbKTYeTxUlRJxmAU0YqmW0IVVUVaOiSjkMFal8FIBcRyG11ZEwy7euZkbhTRz326nsPZOGWp3dukLrSfpGnKxYtHW2lmr5zogLhdMkLgbyjZrKSteqqk2biVUnB12GY18znX6RHHKV6UUeWVco7389pH0NfrO542+i2lKur3hOR0lDAbDWnFCUGY0S4T7dStnDTyymFQ+JRS1MayW5FXLm4p66lcr9EzN4FiZU0FmS5sGxS3ywAdJjEr/a6ywcmC2mUeYGV9pnYCcfVoQ2UUFkQ6kSktXWWMquu8XLckHgX4gxZ+w+KnmZSQj4La1DYKsrFMwpV1dU5NQQaznmuc6Y2UmbkeS0vXuX79NL2kVav5+M7GvYW7dw4xjamnE0BM6n4ERE7Fw21Bvxi3VJopYiLRJvUHRFIpnhRS4jdMoYvKIfZKUu2e0JxiN8Lyk8x3e8ThiLnVhTrr2GjBms5Ss9N0Kh7W12r6uW2UH5npRTOluR2tOGEm95nPQhZbfWY6/XpkX5UbjiRNHLdUxs1+NuYoc6azvmnbaccJTC3NMEo+nZk+QRqSZSr73DwzhRRQOURuyYWlK6wP2vRvndH6o24tsWaMk6NyswZrLgX9LCJJIvqjiD80NwAerx1NIlctn5g2JfSKsQC+NHcbgI4fcq67NiW4HwR2yvRE0EyouVPiUKlE9LOw9qZDT/UY6bclIHIjZqPhJjcAg++U9ZVOCk+vMenUcV+l+zcfdNdVKf6zUVKLZeeVQ14F2tGiqM2ETXBUKfMRsXGK10k+BrNuZkpP6qQC3Ss3pRLGLHhaUoaxxjG1dgAPL94cGx0ar0LlPekSBiM12mj0jAudnJDqAL1huuvVsl3FxGjBJO00Sy267QGtKGWl1x07V89RmcF5pfZpCuiNti3Adz31CvHbbjD3xiIXf/dp1htegmYknhTenoUVzH6SwoORTpLQU57nO85Y8tTtUcj86AFWipzrQjWG+S4bqGYAb05DRkHGOx55fUz2bjJQmISyuXb/wBrebnvAg/pnMJGAVZYOrseYBJ7jFQSBGjmZTN/ZSAXrLPf1ercSLe9nEf0sJHLV9ynymLq+6TvK43RmXiVjxaVDMQrp6Gfuysoivdwn9gqeOvcGj7z7JbKVLrf+9/voFx5JsTETsZ60cBzVAXa9krW0RVoK1quMy4MWD7c6+E7JcqpKvb7/gVtcHbZ5faCWQfyGi81kR9dzSx5evMmcdvY4eGyW6bEPiFtl3N0JZiQxq+2B6hGil9MOU0ZGT1NIAj/ftuzABEVPpxm6YqNnGHn5WOPkulU9AjLJAGZNI20u+Bd+PUW2mgWsZD6RU3G+268bdaP4bzJhi0YSSLHikmXBpulOk4QyafVUlQ5ZGtbBcGnuNnGszV2nXOPIVVNysRYHb64heU5Zi2bvhHHfMMk7rlsStxKidqL8DrUzgDlPM9rpVClF2d6UIPHIOy8SvT9g9MSfJnzjRZ4svs5Lzz1dZxV7egQ1KHz6e1hPnCQtXS4PVUO4FGebEqjmfJ+F0OFmGtIdPaAUa7ZYatwp29AIdXtuyfyp28ydfxNvZkhy5TSrb52p5c8mOchgaOi2BwRBvmndV/0cv96Oq4JNNx7SDlNaUVqXWgxTNfoqSpfBKKqXGDp+XvsQTkuccp2KVpzQXlzFn1FrmFXu1e4vyUh93x4/dZ1H3v0S3gceJLhxlWdufZPV33+apNhQacorte7srapnWyXuCEoqVjPJ6/2Nbf/497zI2Xd+i+Gbp/l/v/eH+dKVh2qxgtAr6hmdTderNaxLew4aq1RzRBBCfAj4ZcAFPiul/OROf1Nby9yBy0WTZsMNqvF2dU8ucouxqaZC1/fdSSOieoFVrYGq1lKKsbrBScxIp2mrlBRevZ5hLKhuZS5vDQWR69D1o1oD0RyjKaA2+o55pTwiJ4PI5HE0M/5GgxZv3TjNtUGHs+2+alRme3WN3+QakKdLFMZKFvT+Qz0y3q6zspWMnAmGQSuhKh1inUk6TKOxe2H+72Yje+rsTaInbpNe+HOED36QkRcSv+3zPPjGdS5dOVcHQ9hfo+XVLOBa6qlsUb/ktF57av4fapo1BBw9BRmRbGPHs1MiTVG6LJ66zcy564SP96AdE7vX1S/fAnT5yb2WYtsK06madt89t6SlVWEctyQuXYJBxnq/Q5b7XOnN8mpPBZ+zkfZjdMLp9ZJOqUZ08QhvZojMPIT2XozTgG5rSNSfZenBawRvHzF4+w/gLb7K3PnnOffaY1wdjssWlpWj1rG9nNUsqLVn00pyeejwaAfe/V2XWHrvi/B9F5i5fIkLw2/TH0W83u/WiTvNRLlN35cgqzV2D4ooirhw4bv3tI/nn//dfTqaw0HILbKd7ieEEC7wCvBDwBXga8BHpJTfnLb96YVF+ez3PMMDDy3x1//uT/O973rXPTxai8ViuTcIIV6QUu5LA+e6nozjO5u12YrBYG3fjucwOFg/kf3jPcCrUso/kFJmwG8AP7LVxu3K5+NL38/7+qf5R3/t53nh+efv2YFaLBbL0USC3OPriHNUpkwfAi433l8B/uhWG/uOmnI53ZrjR88+w6c/9St89jf+w8EeocVisRxhoijiyQtP7mkfL7zw1X06msPhqEyZ/nngg1LKv6rf/wTwHinl32ps83Hg4wAu4pkHwtm6DuBGti4LWb18jw/7sDgFLB/2QRwS9txPJif53L9bSrmV/OyuEEL8Dupa7oVlKeWH9uN4DoOjMkK8AjzceH8OlRZQI6X8DPAZACHE82+mK0d2HnsvCCGeP8pz+HvBnrs995OGEGLf1oOOciDbL47KGuLXgCeEEOeFEAHw48BvHfIxWSwWi+UYcSRGiFLKQgjxN4EvosouPiel/MYhH5bFYrFYjhFHIiACSCl/G/jtO9z8Mwd5LPc59txPJvbcTyYn+dz3nSORVGOxWCwWy0FzVNYQLRaLxWI5UI5dQBRCfEgI8S0hxKtCiF847OPZT4QQDwshviKEuCiE+IYQ4mf05wtCiC8JIb6tf87rz4UQ4lf0tfi6EOKZwz2DvSOEcIUQvyeE+IJ+f14I8Zw+9/+sk64QQoT6/av6948e5nHvFSHEnBDi80KIl/X9f+9Jue9CiJ/Tz/tLQohfF0JEx/W+CyE+J4S4IYR4qfHZru+zEOKjevtvCyE+ehjnchQ5VgFRS7z9a+CHgXcAHxFCvONwj2pfKYC/I6W8ADwL/A19fr8AfFlK+QTwZf0e1HV4Qr8+Dnz63h/yvvMzwMXG+38K/JI+9xXgY/rzjwErUsrHgV/S2x1lfhn4HSnlk8AfQV2DY3/fhRAPAT8NvEtK+RQqqe7HOb73/d8Dk+UPu7rPQogF4BdR4iXvAX7RBFHLDkgpj80LeC/wxcb7TwCfOOzjOsDz/R8ofddvAUv6syXgW/rfv4rSfDXb19sdxReq/vTLwPuBL6C8IJYBb/L+ozKS36v/7entxGGfw12e9wxwafL4T8J9Z0OlakHfxy8AHzzO9x14FHjpbu8z8BHgVxufj21nX1u/jtUIkekSbw8d0rEcKHoq6GngOeABKeVVAP3zjN7suF2Pfwn8PTZcTBeBVSmlseRonl997vr3a3r7o8hjwE3g3+np4s8KIdqcgPsupXwT+BTwHeAq6j6+wMm474bd3udjc//vNcctIE7zeTp2abRCiA7wX4GflVJuZx1+bK6HEOJPATeklC80P56yqbyD3x01POAZ4NNSyqeBARvTZtM4Nueup/p+BDgPPAi0UVOFkxzH+74TW53rSboG+8pxC4g7SrwddYQQPioY/icp5W/qj68LIZb075eAG/rz43Q9vg/4M0KI11FuJ+9HjRjnhBCmnrZ5fvW569/PArfv5QHvI1eAK1LK5/T7z6MC5Em47z8IXJJS3pRS5sBvAu/jZNx3w27v83G6//eU4xYQj7XEmxBCAP8WuCil/BeNX/0WYDLJPopaWzSf/6TORnsWWDNTL0cNKeUnpJTnpJSPou7r/5JS/kXgK8CP6c0mz91ckx/T2x/JXrKU8hpwWQhh3Fv/BPBNTsB9R02VPiuEaOnn35z7sb/vDXZ7n78IfEAIMa9H2B/Qn1l24rAXMff7BXwYZSb8GvD3D/t49vnc/hhq6uPrwO/r14dRayRfBr6tfy7o7QUq6/Y14EVUpt6hn8c+XIcfAL6g//0Y8FXgVeC/AKH+PNLvX9W/f+ywj3uP5/xO4Hl97/87MH9S7jvwj4GXgZeA/wiEx/W+A7+OWivNUSO9j93NfQb+ir4GrwJ/+bDP66i8rFKNxWKxWCwcvylTi8VisVjuChsQLRaLxWLBBkSLxWKxWAAbEC0Wi8ViAWxAtFgsFosFsAHRYrFYLBbABkSLZUuEstu6pN0D0IXOl4QQjwghlowF1S729ykhxPsP5mgtFstesQHRYtkCKeVllKXOJ/VHnwQ+I6V8A/jbwK/tcpf/iu01SC0WyyFiC/Mtlm3Q2rEvAJ8Dfgp4WkqZCSH+ALggpRwJIf4S8GdRXn1PAf8cCICfAEbAh6WUt/X+XgD+pFRybBaL5T7CjhAtlm2QSlD651Fmsz+rg+F5lAntqLHpU8BfQBmy/hNgKJUzxf8BfrKx3f9FCZVbLJb7DBsQLZad+WGUvuRT+v0Syp+wyVeklD0p5U2UB9//1J+/iDJ8NdxA2RhZLJb7DBsQLZZtEEK8E/gh4Fng57T9ToISkW7SHC1WjfcVys/QEOm/t1gs9xk2IFosW6Dthj6Nmir9DvDPUO7trzA+6tsNb0e5NlgslvsMGxAtlq35KeA7Usov6ff/BngSeBfwmhDi8d3sTCfoPI6ycbJYLPcZNsvUYrkLhBA/CnyvlPIf7PJvnpFS/sODOzKLxXK3eDtvYrFYJpFS/jchxOIu/8xDlWRYLJb7EDtCtFgsFosFu4ZosVgsFgtgA6LFYrFYLIANiBaLxWKxADYgWiwWi8UC2IBosVgsFgsA/x+IK41n52GVAQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nx = 100; ny = 100; xsiz = 10.0; ysiz = 10.0; xmn = 5.0; ymn = 5.0; nxdis = 1; nydis = 1\n",
    "ndmin = 0; ndmax = 20; radius = 20; ktype = 0; skmean = 0\n",
    "vario = make_variogram(nug=0.4,nst=1,it1=1,cc1=0.6,azi1=60.0,hmaj1=100,hmin1=100)\n",
    "tmin = -999; tmax = 999\n",
    "x = [1]; y = [0.5]; vr = [-0.5]\n",
    "df = pd.DataFrame({'x':x,'y':y,'vr':vr})\n",
    "\n",
    "sim = sgsim(df,'x','y','vr',wcol=-1,scol=-1,tmin=tmin,tmax=tmax,itrans=0,ismooth=0,dftrans=0,tcol=0,\n",
    "            twtcol=0,zmin=-3.0,zmax=3.0,ltail=1,ltpar=-3.0,utail=1,utpar=3.0,nsim=1,\n",
    "            nx=nx,xmn=xmn,xsiz=xsiz,ny=ny,ymn=ymn,ysiz=ysiz,seed=73073,\n",
    "            ndmin=ndmin,ndmax=ndmax,nodmax=20,mults=0,nmult=2,noct=-1,radius=radius,radius1=1,sang1=0,\n",
    "            mxctx=10,mxcty=10,ktype=ktype,colocorr=0.0,sec_map=0,vario=vario)\n",
    "\n",
    "xmin = 0.0; xmax = 1000.0; ymin = 0.0; ymax = 1000.0; cmap = plt.cm.inferno\n",
    "plt.subplot(221)\n",
    "locpix_st(sim,xmin,xmax,ymin,ymax,xsiz,-3.5,3.5,df,'x','y','vr','Sequential Gaussian Simulation - GeostatsPy','X(m)','Y(m)','NScore Realization',cmap)\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=2.5, wspace=0.2, hspace=0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 579,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(221)\n",
    "locpix_st(sim,xmin,xmax,ymin,ymax,xsiz,-3.5,10000.5,df,'x','y','vr','Sequential Gaussian Simulation - GeostatsPy','X(m)','Y(m)','NScore Realization',cmap)\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=2.5, wspace=0.2, hspace=0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I hope you find this code and demonstration useful. I'm always happy to discuss geostatistics, statistical modeling, uncertainty modeling and machine learning,\n",
    "\n",
    "*Michael*\n",
    "\n",
    "**Michael Pyrcz**, Ph.D., P.Eng. Associate Professor The Hildebrand Department of Petroleum and Geosystems Engineering, Bureau of Economic Geology, The Jackson School of Geosciences, The University of Texas at Austin\n",
    "On Twitter I'm the **GeostatsGuy** and on YouTube my lectures are on the channel, **GeostatsGuy Lectures**."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
